example of conclusion in literature review

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How to Conclude a Literature Review

By Laura Brown on 6th March 2019

The conclusion of the dissertation literature review focuses on a few critical points,

• Highlight the essential parts of the existing body of literature in a concise way.
• Next, you should analyse the current state of the reviewed literature .
• Explain the research gap for your chosen topic/existing knowledge.
• Now, outline the areas for future study by mentioning main agreements and disagreements in the literature.
• Finally, link the research to existing knowledge .

Now, any of you who have been into research would agree that literature review is a very exhausting process and may stress you during your academic career. It is tougher because it requires you to be organised. We have seen many students asking does a literature review need a conclusion.

Well, the answer is simple, a good literature review will always have a proper ending. But there is nothing to worry about how to write a conclusion for a literature review. Here is a complete guide for you in “four” simple yet convenient steps. These steps can really be valuable in providing an excellent presentation to your literature review help . Furthermore, you can ask us for literature review conclusion examples anytime using our live chat or email option.

Now, without further ado, let’s move towards the steps.

How To Write A Literature Review Conclusion

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Here are four major steps which can help you with how to conclude a literature review with ease.

1. Enlist Key Points

The conclusion can also be said as judgement because it gives a clear view of your work, whether you achieved your targeted objectives or not. Typically, it is not too difficult to conclude a review, but it can be challenging as well if not carried out properly.

It is crucial to find key features which should be engaging and useful as well for a reader. So at first, draft or enlist key factors before moving forward towards initialising your summary.

2. Summarise The Key Features Briefly

This is a most sensitive and important step of a dissertation literature review conclusion, where you should stick to the following things to get the job done efficiently.

• Once you are done drafting the important points , here you should mention them briefly.
• You can also take the liberty to agree or disagree with whatever literature you have gone through.
• Make sure you don’t drag your arguments while counter-arguing. Keeping your points specific is key.
• Describe, in one to two lines, how you addressed the previously identified gap .
• It is also important to point out the lapses you have noticed in previous authors’ work. Those lapses could be a misquotation of figures, a wrong pattern of research and so on.
• Alongside this, discuss existing theories and methods to build a framework for your research.

3. Educational Implications Of The Reviewed Literature

After mentioning the key factors, it is suggested to put implications to the already reviewed research. Like, as identifying problems in the already done research and giving recommendations on how these problems can be resolved.

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4. Indicating Room For Future Research

After completing the whole analysis of the particular research, you will be capable of identifying the work which can be done in future. You can also leave some gaps for future researchers so others can extend your work. This will be the final step, and this is how to end a literature review.

Tips That Can Enlighten Your Conclusion

We hope that things are very clear to you on how to write a conclusion for a literature review. If you want it to be even better and more meaningful, then you should keep the below points in mind.

• It should not be burdened with an unnecessary chain of details.
• It should be as precise and easy to understand as possible.
• You should mention important key points and findings .
• Make sure to put all points in a flow so the reader can understand your research in one go.
• Do not add anything from your own.

“Simply put, touch the prominent factors and leave them unexplained here”.

Get Help to Conclude Your Literature Review

If you are able to keep your focus around these steps and mentioned points, believe us, you will never ask anyone how to conclude literature review.

Looking At Literature Review Conclusion Example

Below are three examples which will help you understand how to conclude a literature review.

1. Firstly, you should summarise the important aspects and evaluate the current state of the existing literature.

Overall, the findings from this literature review highlight the need for further research to address the gaps in knowledge on the effectiveness of mindfulness-based interventions for reducing symptoms of anxiety and depression in college students.

2. Now, along with mentioning the gaps, come up with your approach to future study.

Therefore, to address this gap in the literature, we incorporated larger and more diverse samples, used standardised measures of mindfulness and mental health outcomes, and included longer follow-up periods to assess the long-term effects of mindfulness-based interventions on anxiety and depression.

3. Now summarise on how your findings will contribute to the particular field by linking it to the existing knowledge.

The findings from the study will provide important insights for researchers, clinicians, and educators interested in developing and implementing effective interventions to promote mental health and well-being among college students, and highlight the need for further research to establish the effectiveness of mindfulness-based interventions in this population.

We hope that these examples will bring in more clarification and you can have a better idea about the literature review conclusion.

What basically is a literature review?

What are the 3 primary parts of a literature review, what are the goals of writing a literature review.

There are four primary objectives of writing a literature review:

1. Determining the background from the previous scholarly literature related to the topic.

2. Identifying the gaps between literature to boost further research.

3. Analysing if the theory is applicable and associating a suitable methodology.

Why is a literature review conclusion necessary?

• https://azhin.org/cummings/basiclitreview/conclusions
• https://www.citewrite.qut.edu.au/write/writing-well/litreview.html
• https://psychology.ucsd.edu/undergraduate-program/undergraduate-resources/academic-writing-resources/writing-research-papers/writing-lit-review.html
• https://students.unimelb.edu.au/academic-skills/resources/report-writing/reviewing-the-literature

Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.

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What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 

How to write a good literature review 

• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• How to write a literature review faster with Paperpal? 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

1. Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 

2. Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 

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3. Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 

4. Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 

5. Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 

6. Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

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Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

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Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

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How to write a literature review faster with Paperpal?

Paperpal, an AI writing assistant, integrates powerful academic search capabilities within its writing platform. With the Research feature, you get 100% factual insights, with citations backed by 250M+ verified research articles, directly within your writing interface with the option to save relevant references in your Citation Library. By eliminating the need to switch tabs to find answers to all your research questions, Paperpal saves time and helps you stay focused on your writing.   

Here’s how to use the Research feature:  

• Ask a question: Get started with a new document on paperpal.com. Click on the “Research” feature and type your question in plain English. Paperpal will scour over 250 million research articles, including conference papers and preprints, to provide you with accurate insights and citations. 
• Review and Save: Paperpal summarizes the information, while citing sources and listing relevant reads. You can quickly scan the results to identify relevant references and save these directly to your built-in citations library for later access. 
• Cite with Confidence: Paperpal makes it easy to incorporate relevant citations and references into your writing, ensuring your arguments are well-supported by credible sources. This translates to a polished, well-researched literature review. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a good literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. By combining effortless research with an easy citation process, Paperpal Research streamlines the literature review process and empowers you to write faster and with more confidence. Try Paperpal Research now and see for yourself.  

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Report Statistics
• How to Edit Your Work

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Writing: Literature Review Basics

• What is Synthesis?
• Organizing Your Research
• Paraphrasing, Summary, or Direct Quotation?
• Introductions
• Conclusions
• All Writing Guides: Home
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The Job of the Conclusion

The job of the conclusion is, quite literally, to conclude ... or to wrap things up so the reader feels a sense of closure.  It accomplishes this by stepping back from the specifics in order to view the bigger picture of the document. In other words, it is reminding the reader of the main argument.

Whereas an introduction started out generally and moved towards discussion of a specific focus, the conclusion takes the opposite approach.  It starts by reminding the reader of the contents and importance of your findings and then moves out gradually to more general topics.

For most written assignments, the conclusion is a single paragraph.  It does not introduce any new information; rather, it succinctly restates your chief conclusions and places the importance of your findings within your field.  Depending upon the purpose of the literature review, you may also include a brief statement of future directions or self-reflection.

Here is an easy checklist for writing a conclusion:

 Is the main argument of the paper accurately restated as the first sentence (but is not copied verbatim?

In a literature review, you basicaly want to answer the question, "What did I find out? What conclusions did I come to?"   Giving the reader a one-sentence answer to this question that provides a summary of your findings is a solid way to begin a conclusion.

  What recommendations do you have?

Here you may offer the reader your suggestions on what you think should happen next.  You can make recommendations that are specific to the evidence you have uncovered, or you can make recommendations for future research.  When this area is well done, it links to previous conclusions you have already made and gives the conclusion a finished feeling.

 Did you remind the reader of the importance of the topic and how it can contribute to the knowledge in the field?

Make sure that the paper places its findings in the context of some kind of needed change, relevance, or solution.  If you addressed why the topic was interesting, important, or relevant in your introduction, you can loop back to that here.  Other ways that can be done are to remind the reader of other research you have discussed and how your work builds upon theirs, or what gaps there may yet be to explore.

Keep these items in mind as "what not to do":

 Is there a sense of closure without using words such as "In conclusion?"

If you have to use the words "In conclusion" or similar ones to launch your conclusion so the reader knows the end is near, you've got a problem.  Make sure the reader has a distinct sense that the paper has come to an end without telling them it is ending. It is important to not leave the reader hanging. 

 Did you avoid presenting any new information?

No new ideas should be introduced in the conclusion. It is simply a review of the material that is already present in the paper. The only new idea would be the suggesting of a direction for future research.

Stigmatization of the mentally ill is caused by the public’s belief in myths about the dangerousness of the mentally ill and exposing those myths can reduce stigmatization. At least one-third of the people sampled in one study said that they would both reject socially and fear violence from someone displaying behaviors associated with different mentally illnesses. Other research discovered that this rejection is associated to lack of contact with the mentally ill and that as contact increased, fear of the mentally ill decreased. The direction of the relationship between fear and rejection seems to be that fear (possibly based upon myths about mental illness) causes rejection. Taken as a whole, it appears that exposing these myths as myths increases the acceptance of the mentally ill and that staged contact with a mentally person to expose myths has an even more powerful effect. Caution must be advised, though; Martin et al.’s (2002) and Alexander and Link’s (2003) studies and the first study of Corrigan et al. (2002) were based upon paper and pencil methodologies. And while Corrigan et al.’s (2002) second study involved staged Myths of violence 6 presentations, it was conducted in a college setting with a college sample. Future research should replicate these findings in more natural settings with different populations.

Now let's break that down.

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• 04 December 2020
• Correction 09 December 2020

How to write a superb literature review

Andy Tay is a freelance writer based in Singapore.

You can also search for this author in PubMed   Google Scholar

Literature reviews are important resources for scientists. They provide historical context for a field while offering opinions on its future trajectory. Creating them can provide inspiration for one’s own research, as well as some practice in writing. But few scientists are trained in how to write a review — or in what constitutes an excellent one. Even picking the appropriate software to use can be an involved decision (see ‘Tools and techniques’). So Nature asked editors and working scientists with well-cited reviews for their tips.

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doi: https://doi.org/10.1038/d41586-020-03422-x

Interviews have been edited for length and clarity.

Updates & Corrections

Correction 09 December 2020 : An earlier version of the tables in this article included some incorrect details about the programs Zotero, Endnote and Manubot. These have now been corrected.

Hsing, I.-M., Xu, Y. & Zhao, W. Electroanalysis 19 , 755–768 (2007).

Article   Google Scholar  

Ledesma, H. A. et al. Nature Nanotechnol. 14 , 645–657 (2019).

Article   PubMed   Google Scholar  

Brahlek, M., Koirala, N., Bansal, N. & Oh, S. Solid State Commun. 215–216 , 54–62 (2015).

Choi, Y. & Lee, S. Y. Nature Rev. Chem . https://doi.org/10.1038/s41570-020-00221-w (2020).

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Literature Review: Conducting & Writing

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How do I Write a Literature Review?: #5 Writing the Review

• Step #1: Choosing a Topic
• Step #2: Finding Information
• Step #3: Evaluating Content
• Step #4: Synthesizing Content
• #5 Writing the Review
• Citing Your Sources

WRITING THE REVIEW 

You've done the research and now you're ready to put your findings down on paper. When preparing to write your review, first consider how will you organize your review.

The actual review generally has 5 components:

Abstract  -  An abstract is a summary of your literature review. It is made up of the following parts:

• A contextual sentence about your motivation behind your research topic
• Your thesis statement
• A descriptive statement about the types of literature used in the review
• Summarize your findings
• Conclusion(s) based upon your findings

Introduction :   Like a typical research paper introduction, provide the reader with a quick idea of the topic of the literature review:

• Define or identify the general topic, issue, or area of concern. This provides the reader with context for reviewing the literature.
• Identify related trends in what has already been published about the topic; or conflicts in theory, methodology, evidence, and conclusions; or gaps in research and scholarship; or a single problem or new perspective of immediate interest.
• Establish your reason (point of view) for reviewing the literature; explain the criteria to be used in analyzing and comparing literature and the organization of the review (sequence); and, when necessary, state why certain literature is or is not included (scope)  - 

Body :  The body of a literature review contains your discussion of sources and can be organized in 3 ways-

• Chronological -  by publication or by trend
• Thematic -  organized around a topic or issue, rather than the progression of time
• Methodical -  the focusing factor usually does not have to do with the content of the material. Instead, it focuses on the "methods" of the literature's researcher or writer that you are reviewing

You may also want to include a section on "questions for further research" and discuss what questions the review has sparked about the topic/field or offer suggestions for future studies/examinations that build on your current findings.

Conclusion :  In the conclusion, you should:

Conclude your paper by providing your reader with some perspective on the relationship between your literature review's specific topic and how it's related to it's parent discipline, scientific endeavor, or profession.

Bibliography :   Since a literature review is composed of pieces of research, it is very important that your correctly cite the literature you are reviewing, both in the reviews body as well as in a bibliography/works cited. To learn more about different citation styles, visit the " Citing Your Sources " tab.

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How To Structure Your Literature Review

3 options to help structure your chapter.

By: Amy Rommelspacher (PhD) | Reviewer: Dr Eunice Rautenbach | November 2020 (Updated May 2023)

Writing the literature review chapter can seem pretty daunting when you’re piecing together your dissertation or thesis. As  we’ve discussed before , a good literature review needs to achieve a few very important objectives – it should:

• Demonstrate your knowledge of the research topic
• Identify the gaps in the literature and show how your research links to these
• Provide the foundation for your conceptual framework (if you have one)
• Inform your own  methodology and research design

To achieve this, your literature review needs a well-thought-out structure . Get the structure of your literature review chapter wrong and you’ll struggle to achieve these objectives. Don’t worry though – in this post, we’ll look at how to structure your literature review for maximum impact (and marks!).

But wait – is this the right time?

Deciding on the structure of your literature review should come towards the end of the literature review process – after you have collected and digested the literature, but before you start writing the chapter. 

In other words, you need to first develop a rich understanding of the literature before you even attempt to map out a structure. There’s no use trying to develop a structure before you’ve fully wrapped your head around the existing research.

Equally importantly, you need to have a structure in place before you start writing , or your literature review will most likely end up a rambling, disjointed mess. 

Importantly, don’t feel that once you’ve defined a structure you can’t iterate on it. It’s perfectly natural to adjust as you engage in the writing process. As we’ve discussed before , writing is a way of developing your thinking, so it’s quite common for your thinking to change – and therefore, for your chapter structure to change – as you write. 

Need a helping hand?

Like any other chapter in your thesis or dissertation, your literature review needs to have a clear, logical structure. At a minimum, it should have three essential components – an  introduction , a  body   and a  conclusion . 

Let’s take a closer look at each of these.

1: The Introduction Section

Just like any good introduction, the introduction section of your literature review should introduce the purpose and layout (organisation) of the chapter. In other words, your introduction needs to give the reader a taste of what’s to come, and how you’re going to lay that out. Essentially, you should provide the reader with a high-level roadmap of your chapter to give them a taste of the journey that lies ahead.

Here’s an example of the layout visualised in a literature review introduction:

Your introduction should also outline your topic (including any tricky terminology or jargon) and provide an explanation of the scope of your literature review – in other words, what you  will   and  won’t   be covering (the delimitations ). This helps ringfence your review and achieve a clear focus . The clearer and narrower your focus, the deeper you can dive into the topic (which is typically where the magic lies). 

Depending on the nature of your project, you could also present your stance or point of view at this stage. In other words, after grappling with the literature you’ll have an opinion about what the trends and concerns are in the field as well as what’s lacking. The introduction section can then present these ideas so that it is clear to examiners that you’re aware of how your research connects with existing knowledge .

2: The Body Section

The body of your literature review is the centre of your work. This is where you’ll present, analyse, evaluate and synthesise the existing research. In other words, this is where you’re going to earn (or lose) the most marks. Therefore, it’s important to carefully think about how you will organise your discussion to present it in a clear way. 

The body of your literature review should do just as the description of this chapter suggests. It should “review” the literature – in other words, identify, analyse, and synthesise it. So, when thinking about structuring your literature review, you need to think about which structural approach will provide the best “review” for your specific type of research and objectives (we’ll get to this shortly).

There are (broadly speaking)  three options  for organising your literature review.

Option 1: Chronological (according to date)

Organising the literature chronologically is one of the simplest ways to structure your literature review. You start with what was published first and work your way through the literature until you reach the work published most recently. Pretty straightforward.

The benefit of this option is that it makes it easy to discuss the developments and debates in the field as they emerged over time. Organising your literature chronologically also allows you to highlight how specific articles or pieces of work might have changed the course of the field – in other words, which research has had the most impact . Therefore, this approach is very useful when your research is aimed at understanding how the topic has unfolded over time and is often used by scholars in the field of history. That said, this approach can be utilised by anyone that wants to explore change over time .

For example , if a student of politics is investigating how the understanding of democracy has evolved over time, they could use the chronological approach to provide a narrative that demonstrates how this understanding has changed through the ages.

Here are some questions you can ask yourself to help you structure your literature review chronologically.

• What is the earliest literature published relating to this topic?
• How has the field changed over time? Why?
• What are the most recent discoveries/theories?

In some ways, chronology plays a part whichever way you decide to structure your literature review, because you will always, to a certain extent, be analysing how the literature has developed. However, with the chronological approach, the emphasis is very firmly on how the discussion has evolved over time , as opposed to how all the literature links together (which we’ll discuss next ).

Option 2: Thematic (grouped by theme)

The thematic approach to structuring a literature review means organising your literature by theme or category – for example, by independent variables (i.e. factors that have an impact on a specific outcome).

As you’ve been collecting and synthesising literature , you’ll likely have started seeing some themes or patterns emerging. You can then use these themes or patterns as a structure for your body discussion. The thematic approach is the most common approach and is useful for structuring literature reviews in most fields.

For example, if you were researching which factors contributed towards people trusting an organisation, you might find themes such as consumers’ perceptions of an organisation’s competence, benevolence and integrity. Structuring your literature review thematically would mean structuring your literature review’s body section to discuss each of these themes, one section at a time.

Here are some questions to ask yourself when structuring your literature review by themes:

• Are there any patterns that have come to light in the literature?
• What are the central themes and categories used by the researchers?
• Do I have enough evidence of these themes?

PS – you can see an example of a thematically structured literature review in our literature review sample walkthrough video here.

Option 3: Methodological

The methodological option is a way of structuring your literature review by the research methodologies used . In other words, organising your discussion based on the angle from which each piece of research was approached – for example, qualitative , quantitative or mixed  methodologies.

Structuring your literature review by methodology can be useful if you are drawing research from a variety of disciplines and are critiquing different methodologies. The point of this approach is to question  how  existing research has been conducted, as opposed to  what  the conclusions and/or findings the research were.

For example, a sociologist might centre their research around critiquing specific fieldwork practices. Their literature review will then be a summary of the fieldwork methodologies used by different studies.

Here are some questions you can ask yourself when structuring your literature review according to methodology:

• Which methodologies have been utilised in this field?
• Which methodology is the most popular (and why)?
• What are the strengths and weaknesses of the various methodologies?
• How can the existing methodologies inform my own methodology?

3: The Conclusion Section

Once you’ve completed the body section of your literature review using one of the structural approaches we discussed above, you’ll need to “wrap up” your literature review and pull all the pieces together to set the direction for the rest of your dissertation or thesis.

The conclusion is where you’ll present the key findings of your literature review. In this section, you should emphasise the research that is especially important to your research questions and highlight the gaps that exist in the literature. Based on this, you need to make it clear what you will add to the literature – in other words, justify your own research by showing how it will help fill one or more of the gaps you just identified.

Last but not least, if it’s your intention to develop a conceptual framework for your dissertation or thesis, the conclusion section is a good place to present this.

Example: Thematically Structured Review

In the video below, we unpack a literature review chapter so that you can see an example of a thematically structure review in practice.

Let’s Recap

In this article, we’ve  discussed how to structure your literature review for maximum impact. Here’s a quick recap of what  you need to keep in mind when deciding on your literature review structure:

• Just like other chapters, your literature review needs a clear introduction , body and conclusion .
• The introduction section should provide an overview of what you will discuss in your literature review.
• The body section of your literature review can be organised by chronology , theme or methodology . The right structural approach depends on what you’re trying to achieve with your research.
• The conclusion section should draw together the key findings of your literature review and link them to your research questions.

If you’re ready to get started, be sure to download our free literature review template to fast-track your chapter outline.

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

You Might Also Like:

27 Comments

Great work. This is exactly what I was looking for and helps a lot together with your previous post on literature review. One last thing is missing: a link to a great literature chapter of an journal article (maybe with comments of the different sections in this review chapter). Do you know any great literature review chapters?

I agree with you Marin… A great piece

I agree with Marin. This would be quite helpful if you annotate a nicely structured literature from previously published research articles.

Awesome article for my research.

I thank you immensely for this wonderful guide

It is indeed thought and supportive work for the futurist researcher and students

Very educative and good time to get guide. Thank you

Great work, very insightful. Thank you.

Thanks for this wonderful presentation. My question is that do I put all the variables into a single conceptual framework or each hypothesis will have it own conceptual framework?

Thank you very much, very helpful

This is very educative and precise . Thank you very much for dropping this kind of write up .

Pheeww, so damn helpful, thank you for this informative piece.

I’m doing a research project topic ; stool analysis for parasitic worm (enteric) worm, how do I structure it, thanks.

comprehensive explanation. Help us by pasting the URL of some good “literature review” for better understanding.

great piece. thanks for the awesome explanation. it is really worth sharing. I have a little question, if anyone can help me out, which of the options in the body of literature can be best fit if you are writing an architectural thesis that deals with design?

I am doing a research on nanofluids how can l structure it?

Beautifully clear.nThank you!

Lucid! Thankyou!

Brilliant work, well understood, many thanks

I like how this was so clear with simple language 😊😊 thank you so much 😊 for these information 😊

Insightful. I was struggling to come up with a sensible literature review but this has been really helpful. Thank you!

You have given thought-provoking information about the review of the literature.

Thank you. It has made my own research better and to impart your work to students I teach

I learnt a lot from this teaching. It’s a great piece.

I am doing research on EFL teacher motivation for his/her job. How Can I structure it? Is there any detailed template, additional to this?

You are so cool! I do not think I’ve read through something like this before. So nice to find somebody with some genuine thoughts on this issue. Seriously.. thank you for starting this up. This site is one thing that is required on the internet, someone with a little originality!

I’m asked to do conceptual, theoretical and empirical literature, and i just don’t know how to structure it

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How to write a literature review introduction (+ examples)

The introduction to a literature review serves as your reader’s guide through your academic work and thought process. Explore the significance of literature review introductions in review papers, academic papers, essays, theses, and dissertations. We delve into the purpose and necessity of these introductions, explore the essential components of literature review introductions, and provide step-by-step guidance on how to craft your own, along with examples.

Why you need an introduction for a literature review

When you need an introduction for a literature review, what to include in a literature review introduction, examples of literature review introductions, steps to write your own literature review introduction.

A literature review is a comprehensive examination of the international academic literature concerning a particular topic. It involves summarizing published works, theories, and concepts while also highlighting gaps and offering critical reflections.

In academic writing , the introduction for a literature review is an indispensable component. Effective academic writing requires proper paragraph structuring to guide your reader through your argumentation. This includes providing an introduction to your literature review.

It is imperative to remember that you should never start sharing your findings abruptly. Even if there isn’t a dedicated introduction section .

Instead, you should always offer some form of introduction to orient the reader and clarify what they can expect.

There are three main scenarios in which you need an introduction for a literature review:

• Academic literature review papers: When your literature review constitutes the entirety of an academic review paper, a more substantial introduction is necessary. This introduction should resemble the standard introduction found in regular academic papers.
• Literature review section in an academic paper or essay: While this section tends to be brief, it’s important to precede the detailed literature review with a few introductory sentences. This helps orient the reader before delving into the literature itself.
• Literature review chapter or section in your thesis/dissertation: Every thesis and dissertation includes a literature review component, which also requires a concise introduction to set the stage for the subsequent review.

You may also like: How to write a fantastic thesis introduction (+15 examples)

It is crucial to customize the content and depth of your literature review introduction according to the specific format of your academic work.

In practical terms, this implies, for instance, that the introduction in an academic literature review paper, especially one derived from a systematic literature review , is quite comprehensive. Particularly compared to the rather brief one or two introductory sentences that are often found at the beginning of a literature review section in a standard academic paper. The introduction to the literature review chapter in a thesis or dissertation again adheres to different standards.

Here’s a structured breakdown based on length and the necessary information:

Academic literature review paper

The introduction of an academic literature review paper, which does not rely on empirical data, often necessitates a more extensive introduction than the brief literature review introductions typically found in empirical papers. It should encompass:

• The research problem: Clearly articulate the problem or question that your literature review aims to address.
• The research gap: Highlight the existing gaps, limitations, or unresolved aspects within the current body of literature related to the research problem.
• The research relevance: Explain why the chosen research problem and its subsequent investigation through a literature review are significant and relevant in your academic field.
• The literature review method: If applicable, describe the methodology employed in your literature review, especially if it is a systematic review or follows a specific research framework.
• The main findings or insights of the literature review: Summarize the key discoveries, insights, or trends that have emerged from your comprehensive review of the literature.
• The main argument of the literature review: Conclude the introduction by outlining the primary argument or statement that your literature review will substantiate, linking it to the research problem and relevance you’ve established.
• Preview of the literature review’s structure: Offer a glimpse into the organization of the literature review paper, acting as a guide for the reader. This overview outlines the subsequent sections of the paper and provides an understanding of what to anticipate.

By addressing these elements, your introduction will provide a clear and structured overview of what readers can expect in your literature review paper.

Regular literature review section in an academic article or essay

Most academic articles or essays incorporate regular literature review sections, often placed after the introduction. These sections serve to establish a scholarly basis for the research or discussion within the paper.

In a standard 8000-word journal article, the literature review section typically spans between 750 and 1250 words. The first few sentences or the first paragraph within this section often serve as an introduction. It should encompass:

• An introduction to the topic: When delving into the academic literature on a specific topic, it’s important to provide a smooth transition that aids the reader in comprehending why certain aspects will be discussed within your literature review.
• The core argument: While literature review sections primarily synthesize the work of other scholars, they should consistently connect to your central argument. This central argument serves as the crux of your message or the key takeaway you want your readers to retain. By positioning it at the outset of the literature review section and systematically substantiating it with evidence, you not only enhance reader comprehension but also elevate overall readability. This primary argument can typically be distilled into 1-2 succinct sentences.

In some cases, you might include:

• Methodology: Details about the methodology used, but only if your literature review employed a specialized method. If your approach involved a broader overview without a systematic methodology, you can omit this section, thereby conserving word count.

By addressing these elements, your introduction will effectively integrate your literature review into the broader context of your academic paper or essay. This will, in turn, assist your reader in seamlessly following your overarching line of argumentation.

Introduction to a literature review chapter in thesis or dissertation

The literature review typically constitutes a distinct chapter within a thesis or dissertation. Often, it is Chapter 2 of a thesis or dissertation.

Some students choose to incorporate a brief introductory section at the beginning of each chapter, including the literature review chapter. Alternatively, others opt to seamlessly integrate the introduction into the initial sentences of the literature review itself. Both approaches are acceptable, provided that you incorporate the following elements:

• Purpose of the literature review and its relevance to the thesis/dissertation research: Explain the broader objectives of the literature review within the context of your research and how it contributes to your thesis or dissertation. Essentially, you’re telling the reader why this literature review is important and how it fits into the larger scope of your academic work.
• Primary argument: Succinctly communicate what you aim to prove, explain, or explore through the review of existing literature. This statement helps guide the reader’s understanding of the review’s purpose and what to expect from it.
• Preview of the literature review’s content: Provide a brief overview of the topics or themes that your literature review will cover. It’s like a roadmap for the reader, outlining the main areas of focus within the review. This preview can help the reader anticipate the structure and organization of your literature review.
• Methodology: If your literature review involved a specific research method, such as a systematic review or meta-analysis, you should briefly describe that methodology. However, this is not always necessary, especially if your literature review is more of a narrative synthesis without a distinct research method.

By addressing these elements, your introduction will empower your literature review to play a pivotal role in your thesis or dissertation research. It will accomplish this by integrating your research into the broader academic literature and providing a solid theoretical foundation for your work.

Comprehending the art of crafting your own literature review introduction becomes significantly more accessible when you have concrete examples to examine. Here, you will find several examples that meet, or in most cases, adhere to the criteria described earlier.

Example 1: An effective introduction for an academic literature review paper

To begin, let’s delve into the introduction of an academic literature review paper. We will examine the paper “How does culture influence innovation? A systematic literature review”, which was published in 2018 in the journal Management Decision.

The entire introduction spans 611 words and is divided into five paragraphs. In this introduction, the authors accomplish the following:

• In the first paragraph, the authors introduce the broader topic of the literature review, which focuses on innovation and its significance in the context of economic competition. They underscore the importance of this topic, highlighting its relevance for both researchers and policymakers.
• In the second paragraph, the authors narrow down their focus to emphasize the specific role of culture in relation to innovation.
• In the third paragraph, the authors identify research gaps, noting that existing studies are often fragmented and disconnected. They then emphasize the value of conducting a systematic literature review to enhance our understanding of the topic.
• In the fourth paragraph, the authors introduce their specific objectives and explain how their insights can benefit other researchers and business practitioners.
• In the fifth and final paragraph, the authors provide an overview of the paper’s organization and structure.

In summary, this introduction stands as a solid example. While the authors deviate from previewing their key findings (which is a common practice at least in the social sciences), they do effectively cover all the other previously mentioned points.

Example 2: An effective introduction to a literature review section in an academic paper

The second example represents a typical academic paper, encompassing not only a literature review section but also empirical data, a case study, and other elements. We will closely examine the introduction to the literature review section in the paper “The environmentalism of the subalterns: a case study of environmental activism in Eastern Kurdistan/Rojhelat”, which was published in 2021 in the journal Local Environment.

The paper begins with a general introduction and then proceeds to the literature review, designated by the authors as their conceptual framework. Of particular interest is the first paragraph of this conceptual framework, comprising 142 words across five sentences:

“ A peripheral and marginalised nationality within a multinational though-Persian dominated Iranian society, the Kurdish people of Iranian Kurdistan (a region referred by the Kurds as Rojhelat/Eastern Kurdi-stan) have since the early twentieth century been subject to multifaceted and systematic discriminatory and exclusionary state policy in Iran. This condition has left a population of 12–15 million Kurds in Iran suffering from structural inequalities, disenfranchisement and deprivation. Mismanagement of Kurdistan’s natural resources and the degradation of its natural environmental are among examples of this disenfranchisement. As asserted by Julian Agyeman (2005), structural inequalities that sustain the domination of political and economic elites often simultaneously result in environmental degradation, injustice and discrimination against subaltern communities. This study argues that the environmental struggle in Eastern Kurdistan can be asserted as a (sub)element of the Kurdish liberation movement in Iran. Conceptually this research is inspired by and has been conducted through the lens of ‘subalternity’ ” ( Hassaniyan, 2021, p. 931 ).

In this first paragraph, the author is doing the following:

• The author contextualises the research
• The author links the research focus to the international literature on structural inequalities
• The author clearly presents the argument of the research
• The author clarifies how the research is inspired by and uses the concept of ‘subalternity’.

Thus, the author successfully introduces the literature review, from which point onward it dives into the main concept (‘subalternity’) of the research, and reviews the literature on socio-economic justice and environmental degradation.

While introductions to a literature review section aren’t always required to offer the same level of study context detail as demonstrated here, this introduction serves as a commendable model for orienting the reader within the literature review. It effectively underscores the literature review’s significance within the context of the study being conducted.

Examples 3-5: Effective introductions to literature review chapters

The introduction to a literature review chapter can vary in length, depending largely on the overall length of the literature review chapter itself. For example, a master’s thesis typically features a more concise literature review, thus necessitating a shorter introduction. In contrast, a Ph.D. thesis, with its more extensive literature review, often includes a more detailed introduction.

Numerous universities offer online repositories where you can access theses and dissertations from previous years, serving as valuable sources of reference. Many of these repositories, however, may require you to log in through your university account. Nevertheless, a few open-access repositories are accessible to anyone, such as the one by the University of Manchester . It’s important to note though that copyright restrictions apply to these resources, just as they would with published papers.

Master’s thesis literature review introduction

The first example is “Benchmarking Asymmetrical Heating Models of Spider Pulsar Companions” by P. Sun, a master’s thesis completed at the University of Manchester on January 9, 2024. The author, P. Sun, introduces the literature review chapter very briefly but effectively:

PhD thesis literature review chapter introduction

The second example is Deep Learning on Semi-Structured Data and its Applications to Video-Game AI, Woof, W. (Author). 31 Dec 2020, a PhD thesis completed at the University of Manchester . In Chapter 2, the author offers a comprehensive introduction to the topic in four paragraphs, with the final paragraph serving as an overview of the chapter’s structure:

PhD thesis literature review introduction

The last example is the doctoral thesis Metacognitive strategies and beliefs: Child correlates and early experiences Chan, K. Y. M. (Author). 31 Dec 2020 . The author clearly conducted a systematic literature review, commencing the review section with a discussion of the methodology and approach employed in locating and analyzing the selected records.

Having absorbed all of this information, let’s recap the essential steps and offer a succinct guide on how to proceed with creating your literature review introduction:

• Contextualize your review : Begin by clearly identifying the academic context in which your literature review resides and determining the necessary information to include.
• Outline your structure : Develop a structured outline for your literature review, highlighting the essential information you plan to incorporate in your introduction.
• Literature review process : Conduct a rigorous literature review, reviewing and analyzing relevant sources.
• Summarize and abstract : After completing the review, synthesize the findings and abstract key insights, trends, and knowledge gaps from the literature.
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• Systematic Review
• Open access
• Published: 12 May 2024

Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis

• Mingxuan Du 1 ,
• Chengjia Zhao 2 ,
• Haiyan Hu 1 ,
• Ningning Ding 1 ,
• Jiankang He 1 ,
• Wenwen Tian 1 ,
• Wenqian Zhao 1 ,
• Xiujian Lin 1 ,
• Gaoyang Liu 1 ,
• Wendan Chen 1 ,
• ShuangLiu Wang 1 ,
• Pengcheng Wang 3 ,
• Dongwu Xu 1 ,
• Xinhua Shen 4 &
• Guohua Zhang 1  

BMC Psychology volume  12 , Article number:  263 ( 2024 ) Cite this article

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Metrics details

A growing number of studies have reported that problematic social networking use (PSNU) is strongly associated with anxiety symptoms. However, due to the presence of multiple anxiety subtypes, existing research findings on the extent of this association vary widely, leading to a lack of consensus. The current meta-analysis aimed to summarize studies exploring the relationship between PSNU levels and anxiety symptoms, including generalized anxiety, social anxiety, attachment anxiety, and fear of missing out. 209 studies with a total of 172 articles were included in the meta-analysis, involving 252,337 participants from 28 countries. The results showed a moderately positive association between PSNU and generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO) respectively (GA: r  = 0.388, 95% CI [0.362, 0.413]; SA: r  = 0.437, 95% CI [0.395, 0.478]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]), and there were different regulatory factors between PSNU and different anxiety subtypes. This study provides the first comprehensive estimate of the association of PSNU with multiple anxiety subtypes, which vary by time of measurement, region, gender, and measurement tool.

Peer Review reports

Introduction

Social network refers to online platforms that allow users to create, share, and exchange information, encompassing text, images, audio, and video [ 1 ]. The use of social network, a term encompassing various activities on these platforms, has been measured from angles such as frequency, duration, intensity, and addictive behavior, all indicative of the extent of social networking usage [ 2 ]. As of April 2023, there are 4.8 billion social network users globally, representing 59.9% of the world’s population [ 3 ]. The usage of social network is considered a normal behavior and a part of everyday life [ 4 , 5 ]. Although social network offers convenience in daily life, excessive use can lead to PSNU [ 6 , 7 ], posing potential threats to mental health, particularly anxiety symptoms (Rasmussen et al., 2020). Empirical research has shown that anxiety symptoms, including generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO), are closely related to PSNU [ 8 , 9 , 10 , 11 , 12 ]. While some empirical studies have explored the relationship between PSNU and anxiety symptoms, their conclusions are not consistent. Some studies have found a significant positive correlation [ 13 , 14 , 15 ], while others have found no significant correlation [ 16 , 17 , 18 , 19 ]. Furthermore, the degree of correlation varies widely in existing research, with reported r-values ranging from 0.12 to 0.80 [ 20 , 21 ]. Therefore, a systematic meta-analysis is necessary to clarify the impact of PSNU on individual anxiety symptoms.

Previous research lacks a unified concept of PSNU, primarily due to differing theoretical interpretations by various authors, and the use of varied standards and diagnostic tools. Currently, this phenomenon is referred to by several terms, including compulsive social networking use, problematic social networking use, excessive social networking use, social networking dependency, and social networking addiction [ 22 , 23 , 24 , 25 , 26 ]. These conceptual differences hinder the development of a cohesive and systematic research framework, as it remains unclear whether these definitions and tools capture the same underlying construct [ 27 ]. To address this lack of uniformity, this paper will use the term “problematic use” to encompass all the aforementioned nomenclatures (i.e., compulsive, excessive, dependent, and addictive use).

Regarding the relationship between PSNU and anxiety symptoms, two main perspectives exist: the first suggests a positive correlation, while the second proposes a U-shaped relationship. The former perspective, advocating a positive correlation, aligns with the social cognitive theory of mass communication. It posits that PSNU can reinforce certain cognitions, emotions, attitudes, and behaviors [ 28 , 29 ], potentially elevating individuals’ anxiety levels [ 30 ]. Additionally, the cognitive-behavioral model of pathological use, a primary framework for explaining factors related to internet-based addictions, indicates that psychiatric symptoms like depression or anxiety may precede internet addiction, implying that individuals experiencing anxiety may turn to social networking platforms as a coping mechanism [ 31 ]. Empirical research also suggests that highly anxious individuals prefer computer-mediated communication due to the control and social liberation it offers and are more likely to have maladaptive emotional regulation, potentially leading to problematic social network service use [ 32 ]. Turning to the alternate perspective, it proposes a U-shaped relationship as per the digital Goldilocks hypothesis. In this view, moderate social networking usage is considered beneficial for psychosocial adaptation, providing individuals with opportunities for social connection and support. Conversely, both excessive use and abstinence can negatively impact psychosocial adaptation [ 33 ]. In summary, both perspectives offer plausible explanations.

Incorporating findings from previous meta-analyses, we identified seven systematic reviews and two meta-analyses that investigated the association between PSNU and anxiety. The results of these meta-analyses indicated a significant positive correlation between PSNU and anxiety (ranging from 0.33 to 0.38). However, it is evident that these previous meta-analyses had certain limitations. Firstly, they focused only on specific subtypes of anxiety; secondly, they were limited to adolescents and emerging adults in terms of age. In summary, this systematic review aims to ascertain which theoretical perspective more effectively explains the relationship between PSNU and anxiety, addressing the gaps in previous meta-analyses. Additionally, the association between PSNU and anxiety could be moderated by various factors. Drawing from a broad research perspective, any individual study is influenced by researcher-specific designs and associated sample estimates. These may lead to bias compared to the broader population. Considering the selection criteria for moderating variables in empirical studies and meta-analyses [ 34 , 35 ], the heterogeneity of findings on problematic social network usage and anxiety symptoms could be driven by divergence in sample characteristics (e.g., gender, age, region) and research characteristics (measurement instrument of study variables). Since the 2019 coronavirus pandemic, heightened public anxiety may be attributed to the fear of the virus or heightened real life stress. The increased use of electronic devices, particularly smartphones during the pandemic, also instigates the prevalence of problematic social networking. Thus, our analysis focuses on three moderators: sample characteristics (participants’ gender, age, region), measurement tools (for PSNU and anxiety symptoms) and the time of measurement (before COVID-19 vs. during COVID-19).

The present study was conducted in accordance with the 2020 statement on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 36 ]. To facilitate transparency and to avoid unnecessary duplication of research, this study was registered on PROSPERO, and the number is CRD42022350902.

Literature search

Studies on the relationship between the PSNU and anxiety symptoms from 2000 to 2023 were retrieved from seven databases. These databases included China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Information Co. Ltd. (VIP), Web of Science, ScienceDirect, PubMed, and PsycARTICLES. The search strings consisted of (a) anxiety symptoms, (b) social network, and (c) Problematic use. As shown in Table  1 , the keywords for anxiety are as follows: anxiety, generalized anxiety, social anxiety, attachment anxiety, fear of missing out, and FoMO. The keywords for social network are as follows: social network, social media, social networking site, Instagram, and Facebook. The keywords for addiction are as follows: addiction, dependence, problem/problematic use, excessive use. The search deadline was March 19, 2023. A total of 2078 studies were initially retrieved and all were identified ultimately.

Inclusion and exclusion criteria

Retrieved studies were eligible for the present meta-analysis if they met the following inclusion criteria: (a) the study provided Pearson correlation coefficients used to measure the relationship between PSNU and anxiety symptoms; (b) the study reported the sample size and the measurement instruments for the variables; (c) the study was written in English and Chinese; (d) the study provided sufficient statistics to calculate the effect sizes; (e) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, they were coded by the first measurement. In addition, studies were excluded if they: (a) examined non-problematic social network use; (b) had an abnormal sample population; (c) the results of the same sample were included in another study and (d) were case reports or review articles. Two evaluators with master’s degrees independently assessed the eligibility of the articles. A third evaluator with a PhD examined the results and resolved dissenting views.

Data extraction and quality assessment

Two evaluators independently coded the selected articles according to the following characteristics: literature information, time of measurement (before the COVID-19 vs. during the COVID-19), sample source (developed country vs. developing country), sample size, proportion of males, mean age, type of anxiety, and measurement instruments for PSNU and anxiety symptoms. The following principles needed to be adhered to in the coding process: (a) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, it was coded by the first measurement; (b) if multiple studies used the same data, the one with the most complete information was selected; (c) If studies reported t or F values rather than r , the following formula \( r=\sqrt{\frac{{t}^{2}}{{t}^{2}+df}}\) ; \( r=\sqrt{\frac{F}{F+d{f}_{e}}}\) was used to convert them into r values [ 37 , 38 ]. Additionally, if some studies only reported the correlation matrix between each dimension of PSNU and anxiety symptoms, the following formula \( {r}_{xy}=\frac{\sum {r}_{xi}{r}_{yj}}{\sqrt{n+n(n-1){r}_{xixj}}\sqrt{m+m(m-1){r}_{yiyj}}}\) was used to synthesize the r values [ 39 ], where n or m is the number of dimensions of variable x or variable y, respectively, and \( {r}_{xixj} \) or \( {r}_{yiyj}\) represents the mean of the correlation coefficients between the dimensions of variable x or variable y, respectively.

Literature quality was determined according to the meta-analysis quality evaluation scale developed [ 40 ]. The quality of the post-screening studies was assessed by five dimensions: sampling method, efficiency of sample collection, level of publication, and reliability of PSNU and anxiety symptom measurement instruments. The total score of the scale ranged from 0 to 10; higher scores indicated better quality of the literature.

Data analysis

All data were performed using Comprehensive Meta Analysis 3.3 (CMA 3.3). Pearson’s product-moment coefficient r was selected as the effect size index in this meta-analysis. Firstly, \( {\text{F}\text{i}\text{s}\text{h}\text{e}\text{r}}^{{\prime }}\text{s} Z=\frac{1}{2}\times \text{ln}\left(\frac{1+r}{1-r}\right)\) was used to convert the correlation coefficient to Fisher Z . Then the formula \( SE=\sqrt{\frac{1}{n-3}}\) was used to calculate the standard error ( SE ). Finally, the summary of r was obtained from the formula \( r=\frac{{e}^{2z}-1}{{e}^{2z}+1}\) for a comprehensive measure of the relationship between PSNU and anxiety symptoms [ 37 , 41 ].

Although the effect sizes estimated by the included studies may be similar, considering the actual differences between studies (e.g., region and gender), the random effects model was a better choice for data analysis for the current meta-analysis. The heterogeneity of the included study effect sizes was measured for significance by Cochran’s Q test and estimated quantitatively by the I 2 statistic [ 42 ]. If the results indicate there is a significant heterogeneity (the Q test: p -value < 0.05, I 2  > 75) and the results of different studies are significantly different from the overall effect size. Conversely, it indicates there are no differences between the studies and the overall effect size. And significant heterogeneity tends to indicate the possible presence of potential moderating variables. Subgroup analysis and meta-regression analysis were used to examine the moderating effect of categorical and continuous variables, respectively.

Funnel plots, fail-safe number (Nfs) and Egger linear regression were utilized to evaluate the publication bias [ 43 , 44 , 45 ]. The likelihood of publication bias was considered low if the intercept obtained from Egger linear regression was not significant. A larger Nfs indicated a lower risk of publication bias, and if Nfs < 5k + 10 (k representing the original number of studies), publication bias should be a concern [ 46 ]. When Egger’s linear regression was significant, the Duval and Tweedie’s trim-and-fill was performed to correct the effect size. If there was no significant change in the effect size, it was assumed that there was no serious publication bias [ 47 ].

A significance level of P  < 0.05 was deemed applicable in this study.

Sample characteristics

The PRISMA search process is depicted in Fig.  1 . The database search yielded 2078 records. After removing duplicate records and screening the title and abstract, the full text was subject to further evaluation. Ultimately, 172 records fit the inclusion criteria, including 209 independent effect sizes. The present meta-analysis included 68 studies on generalized anxiety, 44 on social anxiety, 22 on attachment anxiety, and 75 on fear of missing out. The characteristics of the selected studies are summarized in Table  2 . The majority of the sample group were adults. Quality scores for selected studies ranged from 0 to 10, with only 34 effect sizes below the theoretical mean, indicating high quality for the included studies. The literature included utilized BSMAS as the primary tool to measure PSNU, DASS-21-A to measure GA, IAS to measure SA, ECR to measure AA, and FoMOS to measure FoMO.

Flow chart of the search and selection strategy

Overall analysis, homogeneity tests and publication bias

As shown in Table  3 , there was significant heterogeneity between PSNU and all four anxiety symptoms (GA: Q  = 1623.090, I 2  = 95.872%; SA: Q  = 1396.828, I 2  = 96.922%; AA: Q  = 264.899, I 2  = 92.072%; FoMO: Q  = 1847.110, I 2  = 95.994%), so a random effects model was chosen. The results of the random effects model indicate a moderate positive correlation between PSNU and anxiety symptoms (GA: r  = 0.350, 95% CI [0.323, 0.378]; SA: r  = 0.390, 95% CI [0.347, 0.431]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]).

Figure  2 shows the funnel plot of the relationship between PSNU and anxiety symptoms. No significant symmetry was seen in the funnel plot of the relationship between PSNU and GA and between PSNU and SA. And the Egger’s regression results also indicated that there might be publication bias ( t  = 3.775, p  < 0.001; t  = 2.309, p  < 0.05). Therefore, it was necessary to use fail-safe number (Nfs) and the trim and fill method for further examination and correction. The Nfs for PSNU and GA as well as PSNU and SA are 4591 and 7568, respectively. Both Nfs were much larger than the standard 5 k  + 10. After performing the trim and fill method, 14 effect sizes were added to the right side of the funnel plat (Fig.  2 .a), the correlation coefficient between PSNU and GA changed to ( r  = 0.388, 95% CI [0.362, 0.413]); 10 effect sizes were added to the right side of the funnel plat (Fig.  2 .b), the correlation coefficient between PSNU and SA changed to ( r  = 0.437, 95% CI [0.395, 0.478]). The correlation coefficients did not change significantly, indicating that there was no significant publication bias associated with the relationship between PSNU and these two anxiety symptoms (GA and SA).

Funnel plot of the relationship between PSNU and anxiety symptoms. Note: Black dots indicated additional studies after using trim and fill method; ( a ) = Funnel plot of the PSNU and GA; ( b ) = Funnel plot of the PSNU and SA; ( c ) = Funnel plot of the PSNU and AA; ( d ) = Funnel plot of the PSNU and FoMO

Sensitivity analyses

Initially, the findings obtained through the one-study-removed approach indicated that the heterogeneities in the relationship between PSNU and anxiety symptoms were not attributed to any individual study. Nevertheless, it is important to note that sensitivity analysis should be performed based on literature quality [ 223 ] since low-quality literature could potentially impact result stability. In the relationship between PSNU and GA, the 10 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.402, 95% CI [0.375, 0.428]); In the relationship between PSNU and SA, the 8 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.431, 95% CI [0.387, 0.472]); In the relationship between PSNU and AA, the 5 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.367, 95% CI [0.298, 0.433]); In the relationship between PSNU and FoMO, the 11 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.508, 95% CI [0.470, 0.544]). The revised estimates indicate that meta-analysis results were stable.

Moderator analysis

The impact of moderator variables on the relation between psnu and ga.

The results of subgroup analysis and meta-regression are shown in Table  4 , the time of measurement significantly moderated the correlation between PSNU and GA ( Q between = 19.268, df  = 2, p  < 0.001). The relation between the two variables was significantly higher during the COVID-19 ( r  = 0.392, 95% CI [0.357, 0.425]) than before the COVID-19 ( r  = 0.270, 95% CI [0.227, 0.313]) or measurement time uncertain ( r  = 0.352, 95% CI [0.285, 0.415]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). The relation was significantly higher when PSNU was measured with the BSMAS ( r  = 0.373, 95% CI [0.341, 0.404]) compared to others ( r  = 0.301, 95% CI [0.256, 0.344]).

The moderating effect of the GA measurement was significant ( Q between = 60.061, df  = 5, p  < 0.001). Specifically, when GA measured by the GAD ( r  = 0.398, 95% CI [0.356, 0.438]) and the DASS-21-A ( r  = 0.433, 95% CI [0.389, 0.475]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the STAI ( r  = 0.232, 95% CI [0.187, 0.276]).

For the relation between PSNU and GA, the moderating effect of region, gender and age were not significant.

The impact of moderator variables on the relation between PSNU and SA

The effects of the moderating variables in the relation between PSNU and SA were shown in Table  5 . The results revealed a gender-moderated variances between the two variables (b = 0.601, 95% CI [ 0.041, 1.161], Q model (1, k = 41) = 4.705, p  = 0.036).

For the relation between PSNU and SA, the moderating effects of time of measurement, region, measurement of PSNU and SA, and age were not significant.

The impact of moderator variables on the relation between PSNU and AA

The effects of the moderating variables in the relation between PSNU and AA were shown in Table  6 , region significantly moderated the correlation between PSNU and AA ( Q between = 6.410, df  = 2, p  = 0.041). The correlation between the two variables was significantly higher in developing country ( r  = 0.378, 95% CI [0.304, 0.448]) than in developed country ( r  = 0.242, 95% CI [0.162, 0.319]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). Specifically, when AA was measured by the GPIUS-2 ( r  = 0.484, 95% CI [0.200, 0.692]) and the PMSMUAQ ( r  = 0.443, 95% CI [0.381, 0.501]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the BSMAS ( r  = 0.248, 95% CI [0.161, 0.331]) and others ( r  = 0.313, 95% CI [0.250, 0.372]).

The moderating effect of the AA measurement was significant ( Q between = 17.283, df  = 2, p  < 0.001). The correlation was significantly higher when measured using the ECR ( r  = 0.386, 95% CI [0.338, 0.432]) compared to the RQ ( r  = 0.200, 95% CI [0.123, 0.275]).

For the relation between PSNU and AA, the moderating effects of time of measurement, region, gender, and age were not significant.

The impact of moderator variables on the relation between PSNU and FoMO

The effects of the moderating variables in the relation between PSNU and FoMO were shown in Table  7 , the moderating effect of the PSNU measurement was significant ( Q between = 8.170, df  = 2, p  = 0.017). Among the sub-dimensions, the others was excluded because there was only one sample. Specifically, when measured using the FoMOS-MSME ( r  = 0.630, 95% CI [0.513, 0.725]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the FoMOS ( r  = 0.472, 95% CI [0.432, 0.509]) and the T-S FoMOS ( r  = 0.557, 95% CI [0.463, 0.639]).

For the relationship between PSNU and FoMO, the moderating effects of time of measurement, region, measurement of PSNU, gender and age were not significant.

Through systematic review and meta-analysis, this study established a positive correlation between PSNU and anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out), confirming a linear relationship and partially supporting the Social Cognitive Theory of Mass Communication [ 28 ] and the Cognitive Behavioral Model of Pathological Use [ 31 ]. Specifically, a significant positive correlation between PSNU and GA was observed, implying that GA sufferers might resort to social network for validation or as an escape from reality, potentially alleviating their anxiety. Similarly, the meta-analysis demonstrated a strong positive correlation between PSNU and SA, suggesting a preference for computer-mediated communication among those with high social anxiety due to perceived control and liberation offered by social network. This preference is often accompanied by maladaptive emotional regulation, predisposing them to problematic use. In AA, a robust positive correlation was found with PSNU, indicating a higher propensity for such use among individuals with attachment anxiety. Notably, the study identified the strongest correlation in the context of FoMO. FoMO’s significant association with PSNU is multifaceted, stemming from the real-time nature of social networks that engenders a continuous concern about missing crucial updates or events. This drives frequent engagement with social network, thereby establishing a direct link to problematic usage patterns. Additionally, social network’s feedback loops amplify this effect, intensifying FoMO. The culture of social comparison on these platforms further exacerbates FoMO, as users frequently compare their lives with others’ selectively curated portrayals, enhancing both their social networking usage frequency and the pursuit for social validation. Furthermore, the integral role of social network in modern life broadens FoMO’s scope, encompassing anxieties about staying informed and connected.

The notable correlation between FoMO and PSNU can be comprehensively understood through various perspectives. FoMO is inherently linked to the real-time nature of social networks, which cultivates an ongoing concern about missing significant updates or events in one’s social circle [ 221 ]. This anxiety prompts frequent engagement with social network, leading to patterns of problematic use. Moreover, the feedback loops in social network algorithms, designed to enhance user engagement, further intensify this fear [ 224 ]. Additionally, social comparison, a common phenomenon on these platforms, exacerbates FoMO as users continuously compare their lives with the idealized representations of others, amplifying feelings of missing out on key social experiences [ 225 ]. This behavior not only increases social networking usage but also is closely linked to the quest for social validation and identity construction on these platforms. The extensive role of social network in modern life further amplifies FoMO, as these platforms are crucial for information exchange and maintaining social ties. FoMO thus encompasses more than social concerns, extending to anxieties about staying informed with trends and dynamics within social networks [ 226 ]. The multifaceted nature of FoMO in relation to social network underscores its pronounced correlation with problematic social networking usage. In essence, the combination of social network’s intrinsic characteristics, psychological drivers of user behavior, the culture of social comparison, and the pervasiveness of social network in everyday life collectively make FoMO the most pronouncedly correlated anxiety type with PSNU.

Additionally, we conducted subgroup analyses on the timing of measurement (before COVID-19 vs. during COVID-19), measurement tools (for PSNU and anxiety symptoms), sample characteristics (participants’ region), and performed a meta-regression analysis on gender and age in the context of PSNU and anxiety symptoms. It was found that the timing of measurement, tools used for assessing PSNU and anxiety, region, and gender had a moderating effect, whereas age did not show a significant moderating impact.

Firstly, the relationship between PSNU and anxiety symptoms was significantly higher during the COVID-19 period than before, especially between PSNU and GA. However, the moderating effect of measurement timing was not significant in the relationship between PSNU and other types of anxiety. This could be attributed to the increased uncertainty and stress during the pandemic, leading to heightened levels of general anxiety [ 227 ]. The overuse of social network for information seeking and anxiety alleviation might have paradoxically exacerbated anxiety symptoms, particularly among individuals with broad future-related worries [ 228 ]. While the COVID-19 pandemic altered the relationship between PSNU and GA, its impact on other types of anxiety (such as SA and AA) may not have been significant, likely due to these anxiety types being more influenced by other factors like social skills and attachment styles, which were minimally impacted by the epidemic.

Secondly, the observed variance in the relationship between PSNU and AA across different economic contexts, notably between developing and developed countries, underscores the multifaceted influence of socio-economic, cultural, and technological factors on this dynamic. The amplified connection in developing countries may be attributed to greater socio-economic challenges, distinct cultural norms regarding social support and interaction, rising social network penetration, especially among younger demographics, and technological disparities influencing accessibility and user experience [ 229 , 230 ]. Moreover, the role of social network as a coping mechanism for emotional distress, potentially fostering insecure attachment patterns, is more pronounced in these settings [ 231 ]. These findings highlight the necessity of considering contextual variations in assessing the psychological impacts of social network, advocating for a nuanced understanding of how socio-economic and cultural backgrounds mediate the relationship between PSNU and mental health outcomes [ 232 ]. Additionally, the relationship between PSNU and other types of anxiety (such as GA and SA) presents uniform characteristics across different economic contexts.

Thirdly, the significant moderating effects of measurement tools in the context of PSNU and its correlation with various forms of anxiety, including GA, and AA, are crucial in interpreting the research findings. Specifically, the study reveals that the Bergen Social Media Addiction Scale (BSMAS) demonstrates a stronger correlation between PSNU and GA, compared to other tools. Similarly, for AA, the Griffiths’ Problematic Internet Use Scale 2 (GPIUS2) and the Problematic Media Social Media Use Assessment Questionnaire (PMSMUAQ) show a more pronounced correlation with AA than the BSMAS or other instruments, but for SA and FoMO, the PSNU instrument doesn’t significantly moderate the correlation. The PSNU measurement tool typically contains an emotional change dimension. SA and FoMO, due to their specific conditional stimuli triggers and correlation with social networks [ 233 , 234 ], are likely to yield more consistent scores in this dimension, while GA and AA may be less reliable due to their lesser sensitivity to specific conditional stimuli. Consequently, the adjustment effects of PSNU measurements vary across anxiety symptoms. Regarding the measurement tools for anxiety, different scales exhibit varying degrees of sensitivity in detecting the relationship with PSNU. The Generalized Anxiety Disorder Scale (GAD) and the Depression Anxiety Stress Scales 21 (DASS-21) are more effective in illustrating a strong relationship between GA and PSNU than the State-Trait Anxiety Inventory (STAI). In the case of AA, the Experiences in Close Relationships-21 (ECR-21) provides a more substantial correlation than the Relationship Questionnaire (RQ). Furthermore, for FoMO, the Fear of Missing Out Scale - Multi-Social Media Environment (FoMOS-MSME) is more indicative of a strong relationship with PSNU compared to the standard FoMOS or the T-S FoMOS. These findings underscore the importance of the selection of appropriate measurement tools in research. Different tools, due to their unique design, focus, and sensitivity, can reveal varying degrees of correlation between PSNU and anxiety disorders. This highlights the need for careful consideration of tool characteristics and their potential impact on research outcomes. It also cautions against drawing direct comparisons between studies without acknowledging the possible variances introduced by the use of different measurement instruments.

Fourthly, the significant moderating role of gender in the relationship between PSNU and SA, particularly pronounced in samples with a higher proportion of females. Women tend to engage more actively and emotionally with social network, potentially leading to an increased dependency on these platforms when confronting social anxiety [ 235 ]. This intensified use might amplify the association between PSNU and SA. Societal and cultural pressures, especially those related to appearance and social status, are known to disproportionately affect women, possibly exacerbating their experience of social anxiety and prompting a greater reliance on social network for validation and support [ 236 ]. Furthermore, women’s propensity to seek emotional support and express themselves on social network platforms [ 237 ] could strengthen this link, particularly in the context of managing social anxiety. Consequently, the observed gender differences in the relationship between PSNU and SA underscore the importance of considering gender-specific dynamics and cultural influences in psychological research related to social network use. In addition, gender consistency was observed in the association between PSNU and other types of anxiety, indicating no significant gender disparities.

Fifthly, the absence of a significant moderating effect of age on the relationship between PSNU and various forms of anxiety suggests a pervasive influence of social network across different age groups. This finding indicates that the impact of PSNU on anxiety is relatively consistent, irrespective of age, highlighting the universal nature of social network’s psychological implications [ 238 ]. Furthermore, this uniformity suggests that other factors, such as individual psychological traits or socio-cultural influences, might play a more crucial role in the development of anxiety related to social networking usage than age [ 239 ]. The non-significant role of age also points towards a potential generational overlap in social networking usage patterns and their psychological effects, challenging the notion that younger individuals are uniquely susceptible to the adverse effects of social network on mental health [ 240 ]. Therefore, this insight necessitates a broader perspective in understanding the dynamics of social network and mental health, one that transcends age-based assumptions.

Limitations

There are some limitations in this research. First, most of the studies were cross-sectional surveys, resulting in difficulties in inferring causality of variables, longitudinal study data will be needed to evaluate causal interactions in the future. Second, considerable heterogeneity was found in the estimated results, although heterogeneity can be partially explained by differences in study design (e.g., Time of measurement, region, gender, and measurement tools), but this can introduce some uncertainty in the aggregation and generalization of the estimated results. Third, most studies were based on Asian samples, which limits the generality of the results. Fourth, to minimize potential sources of heterogeneity, some less frequently used measurement tools were not included in the classification of measurement tools, which may have some impact on the results of heterogeneity interpretation. Finally, since most of the included studies used self-reported scales, it is possible to get results that deviate from the actual situation to some extent.

This meta-analysis aims to quantifies the correlations between PSNU and four specific types of anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out). The results revealed a significant moderate positive association between PSNU and each of these anxiety symptoms. Furthermore, Subgroup analysis and meta-regression analysis indicated that gender, region, time of measurement, and instrument of measurement significantly influenced the relationship between PSNU and specific anxiety symptoms. Specifically, the measurement time and GA measurement tools significantly influenced the relationship between PSNU and GA. Gender significantly influenced the relationship between PSNU and SA. Region, PSNU measurement tools, and AA measurement tools all significantly influenced the relationship between PSNU and AA. The FoMO measurement tool significantly influenced the relationship between PSNU and FoMO. Regarding these findings, prevention interventions for PSNU and anxiety symptoms are important.

Data availability

The datasets are available from the corresponding author on reasonable request.

Abbreviations

• Problematic social networking use
• Generalized anxiety
• Social anxiety
• Attachment anxiety

Fear of miss out

Bergen Social Media Addiction Scale

Facebook Addiction Scale

Facebook Intrusion Questionnaire

Generalized Problematic Internet Use Scale 2

Problematic Mobile Social Media Usage Assessment Questionnaire

Social Network Addiction Tendency Scale

Brief Symptom Inventory

The anxiety subscale of the Depression Anxiety Stress Scales

Generalized Anxiety Disorder

The anxiety subscale of the Hospital Anxiety and Depression Scale

State-Trait Anxiety Inventory

Interaction Anxiousness Scale

Liebowitz Social Anxiety Scale

Social Anxiety Scale for Social Media Users

Social Anxiety for Adolescents

Social Anxiety Subscale of the Self-Consciousness Scale

Social Interaction Anxiety Scale

Experiences in Close Relationship Scale

Relationship questionnaire

Fear of Missing Out Scale

FoMO Measurement Scale in the Mobile Social Media Environment

Trait-State Fear of missing Out Scale

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This research was supported by the Social Science Foundation of China (Grant Number: 23BSH135).

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Mingxuan Du, Haiyan Hu, Ningning Ding, Jiankang He, Wenwen Tian, Wenqian Zhao, Xiujian Lin, Gaoyang Liu, Wendan Chen, ShuangLiu Wang, Dongwu Xu & Guohua Zhang

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Du, M., Zhao, C., Hu, H. et al. Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis. BMC Psychol 12 , 263 (2024). https://doi.org/10.1186/s40359-024-01705-w

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Neighborhood based computational approaches for the prediction of lncRNA-disease associations

• Mariella Bonomo 1 &
• Simona E. Rombo 1 , 2  

BMC Bioinformatics volume  25 , Article number:  187 ( 2024 ) Cite this article

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Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.

We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.

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Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

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Introduction

More than \(98\%\) of the human genome consists of non-coding regions, considered in the past as “junk” DNA. However, in the last decades evidence has been shown that non-coding genome elements often play an important role in regulating various critical biological processes [ 1 ]. An important class of non-coding molecules which have started to receive great attention in the last few years is represented by long non-coding RNAs (lncRNAs), that is, RNAs not translated into functional proteins, and longer than 200 nucleotides.

LncRNAs have been found to interplay with other molecules in order to perform important biological tasks, such as modulating chromatin function, regulating the assembly and function of membraneless nuclear bodies, interfering with signalling pathways [ 2 , 3 ]. Many of these functions ultimately affect gene expression in diverse biological and physiopathological contexts, such as in neuronal disorders, immune responses and cancer. Therefore, the alteration and dysregulation of lncRNAs have been associated with the occurrence and progress of many complex diseases [ 4 ].

The discovery of novel lncRNA-disease associations (LDAs) may provide valuable input to the understanding of disease mechanisms at lncRNA level, as well as to the detection of disease biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, verifying that a specific lncRNA may have a role in the occurrence/progress of a given disease is an expensive process, therefore the number of disease-related lncRNAs verified by traditional biological experiments is yet very limited. Computational approaches for the prediction of potential LDAs can effectively decrease the time and cost of biological experiments, allowing for the identification of the most promising lncRNA-disease pairs to be further verified in laboratory (see [ 5 ] for a comprehensive review on the topic). Such approaches often train predictive models on the basis of the known and experimentally validated lncRNA-disease pairs (e.g., [ 6 , 7 , 8 , 9 ]). In other cases, they rely on the analysis of lncRNAs related information stored in public databases, such as their interaction with other types of molecules (e.g., [ 10 , 11 , 12 , 13 , 14 , 15 ]). As an example, large amounts of lncRNA-miRNA interactions have been collected in public databases, and plenty of experimentally confirmed miRNA-disease associations are available as well. However, although non-coding RNA function and its association with human complex diseases have been widely studied in the literature (see [ 16 , 17 , 18 ]), how to provide biologists with more accurate and ready-to-use software tools for LDAs prediction is yet an open challenge, due to the specific characteristics of lncRNAs (e.g., they are much less characterized than other non-coding RNAs.)

We propose three novel computational approaches for the prediction of LDAs, relying on the use of known lncRNA-miRNA interactions (LMIs) and miRNA-disease associations (MDAs). In particular, we model the problem of LDAs prediction as a neighborhood analysis performed on tripartite graphs, where the three sets of vertices represent lncRNAs, miRNAs and diseases, respectively, and vertices are linked according to LMIs and MDAs. Based on the assumption that similar lncRNAs interact with similar diseases [ 12 ], the first approach proposed here (NGH) aims at identifying novel LDAs by analyzing the behaviour of lncRNAs which are neighbors , in terms of their intermediate relationships with miRNAs. The main idea here is that neighborhood analysis automatically guides towards the detection of similar behaviours, and without the need of using a-priory known LDAs for training. Therefore, differently than other approaches from the literature, those proposed here do not involve verified LDAs in the prediction step, thus avoiding possible biases due to the fact that the number and variety of verified LDAs is yet very limited. The second presented approach (CF) relies on collaborative filtering, applied on the basis of common miRNAs shared by different lncRNAs. We have also explored the combination of neighborhood analysis with collaborative filtering, showing that this notably improves the LDAs prediction accuracy. Indeed, the third approach we have designed (NGH-CF) boosts NGH with collaborative filtering, and it is the best performing one, although also NGH and CF have been able to reach high accuracy values across all the different considered validation tests. In particular, Fig.  1 summarizes the research flowchart explained above.

Flowchart of the research pipeline. The miRNA-lncRNA interactions and miRNA-disease associations are exploited for the construction of the tripartite graph. The tripartite graph, in its turn, is at the basis of both neighborhood analysis and collaborative filtering steps, from which the three proposed approaches are obtained: NGH from neighborhood analysis, CF from collaborative filtering, NGH-CF from the combination of the two ones. Each prediction approach returns in output a LDAs rank

The proposed approaches have been exhaustively validated on both synthetic and real datasets, and the result is that they outperform (also significantly) the other methods from the literature. The experimental analysis shows that the improvement in accuracy achieved by the methods proposed here is due to their ability in capturing specific situations neglected by competitors. Examples of that are represented by true LDAs, detected by our approaches and not by the other approaches in the literature, where the involved lncRNA does not present intermediate molecules in common with the associated disease, although its neighbor lncRNAs share a large number of miRNAs with that disease. Moreover, it is shown that our approaches are robust to noise obtained by perturbing a controlled percentage of lncRNA-miRNA interactions and miRNA-disease associations, with NGH-CF the best one also for robustness. The obtained experimental results show that the prediction methods proposed here may effectively support biologists in selecting significant associations to be further verified in laboratory.

Novel putative LDAs coming from the consensus of the three proposed methods, and not yet registered in the available databases as experimentally verified, are provided. Interestingly, the core of novel LDAs returned with highest score by all three approaches finds evidence in the recent literature, while many other high scored predicted LDAs involve less studied lncRNAs, thus providing useful insights for their better characterization.

A first group of approaches aim at using existing true validated cases to train the prediction system, in order to make it able to correctly detect novel cases.

In [ 19 ] a Laplacian Regularized Least Squares is proposed to infer candidates LDAs ( LRLSLDA ) by applying a semi-supervised learning framework. LRLSLDA assumes that similar diseases tend to correlate with functionally similar lncRNAs, and vice versa. Thus, known LDAs and lncRNA expression profiles are combined to prioritize disease-associated lncRNA candidates by LRLSLDA, which does not require negative samples (i.e., confirmed uncorrelated LDAs). In [ 20 ] the method SKF-LDA is proposed that constructs a lncRNA-disease correlation matrix, based on the known LDAs. Then, it calculates the similarity between lncRNAs and that between diseases, according to specific metrics, and integrates such data. Finally, a predicted LDA matrix is obtained by the Laplacian Regularized Least Squares method. The method ENCFLDA [ 6 ] combines matrix decomposition and collaborative filtering. It uses matrix factorization combined with elastic networks and a collaborative filtering algorithm, making the prediction model more stable and eliminating the problem of data over-fitting. HGNNLDA recently proposed in [ 21 ] is based on hypergraph neural network, where the associations are modeled as a lncRNA-drug bipartite graph to build lncRNA hypergraph and drug hypergraph. Hypergraph convolution is then used to learn correlation of higher-order neighbors from the lncRNA and drug hypergraphs. LDAI-ISPS proposed in [ 22 ] is a LDAs inference approach based on space projections of integrated networks, recostructing the disease (lncRNA) integrated similarities network via integrating multiple information, such as disease semantic similarities, lncRNA functional similarities, and known LDAs. A space projection score is finally obtained via vector projections of the weighted networks. In [ 7 ] a consensual prediction approach called HOPEXGB is presented, to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding and extreme gradient boosting. The authors build a heterogeneous disease-miRNA-lncRNA (DML) information network by linking lncRNA, miRNA, and disease nodes based on their correlation, and generate a negative dataset based on the similarities between unknown and known associations, in order to reduce the false negative rate in the data set for model construction. The method MAGCNSE proposed in [ 23 ] builds multiple feature matrices based on semantic similarity and disease Gaussian interaction profile kernel similarity of both lncRNAs and diseases. MAGCNSE adaptively assigns weights to the different feature matrices built upon the lncRNAs and diseases similarities. Then, it uses a convolutional neural network to further extract features from multi-channel feature matrices, in order to obtain the final representations of lncRNAs and diseases that is used for the LDAs prediction task.

LDAFGAN [ 8 ] is a model designed for predicting associations between long non-coding RNAs (lncRNAs) and diseases. This method is based on a generative and a discriminative networks, typically implemented as multilayer fully connected neural networks, which generate synthetic data based on some underlying distribution. The generative and discriminative networks are trained together in an adversarial manner. The generative network tries to generate realistic representations of lncRNA-disease associations, while the discriminative network tries to distinguish between real and fake associations. This adversarial training process helps the generative network learn to generate more realistic associations. Once the model is trained, it can predict associations between new lncRNAs and diseases without requiring associated data for those specific lncRNAs. The model captures the data distribution during training, which enables it to make predictions even for unseen lncRNAs. The approach GCNFORMER [ 9 ] is based on graph convolutional network and transformer. First, it integrates the intraclass similarity and interclass connections between miRNAs, lncRNAs and diseases, building a graph adjacency matrix. Then, the method extracts the features between various nodes, by a graph convolutional network. To obtain the global dependencies between inputs and outputs, a transformer encoder with a multiheaded attention mechanism to forecast lncRNA-disease associations is finally applied.

As for the approaches summarized above, it is worth to point out that they may suffer of the fact that the experimentally verified LDAs are still very limited, therefore the training set may be rather incomplete and not enough diversified. For this reason, when such approaches are applied for de novo LDAs prediction, their performance may drastically go down [ 12 ].

Other approaches from the literature use intermediate molecules (e.g., miRNA) to infer novel LDAs. Such approaches are the most related to those we propose here.

The author in [ 11 ] proposes HGLDA , relying on HyperGeometric distribution for LDAs inference, that integrates MDAs and LMIs information. HGLDA has been successfully applied to predict Breast Cancer, Lung Cancer and Colorectal Cancer-related lncRNAs. NcPred [ 10 ] is a resource propagation technique, using a tripartite network where the edges associate each lncRNA with a disease through its targets. The algorithm proposed in [ 10 ] is based on a multilevel resource transfer technique, which computes the weights between each lncRNA-disease pair and, at each step, considers the resource transferred from the previous step. The approach in [ 24 ], referred to as LDA-TG for short in the following, is the antecedent of the approaches proposed here. It relies on the construction of a tripartite graph, built upon MDAs and LMIs. A score is assigned to each possible LDA ( l ,  d ) by considering both their respective interactions with common miRNAs, and the interactions with miRNAs shared by the considered disease d and other lncRNAs in the neighborhood of l on the tripartite graph. The approaches proposed here differ from LDA-TG for two main reasons. First, the score of LDA-TG is different from the one we introduce here, that allows to reach a better accuracy. Second, a further step based on collaborative filtering is considered here, which also improves the accuracy performance. A method for LDAs prediction relying on a matrix completion technique inspired by recommender systems is presented in [ 14 ]. A two-layer multi-weighted nearest-neighbor prediction model is adopted, using a method similar to memory-based collaborative filtering. Weights are assigned to neighbors for reassigning values to the target matrix, that is an adjacency matrix consisting of lncRNAs, diseases and miRNA. SSMF-BLNP [ 25 ] is based on the combination of selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP). In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. In BLNP, the initial LDAs are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation.

A third category includes approaches based on integrative frameworks, proposed to take into account different types of information related to lncRNAs, such as their interactions with other molecules, their involvement in disorders and diseases, their similarities. This may improve the prediction step, taking into account simultaneously independent factors.

IntNetLncSim [ 26 ] relies on the construction of an integrated network that comprises lncRNA regulatory data, miRNA-mRNA and mRNA-mRNA interactions. The method computes a similarity score for all pairs of lncRNAs in the integrated network, then analyzes the information flow based on random walk with damping. This allows to infer novel LDAs by exploring the function of lncRNAs. SIMCLDA [ 12 ] identifies LDAs by using inductive matrix completion, based on the integration of known LDAs, disease-gene interactions and gene-gene interactions. The main idea in [ 12 ] is to extract feature vectors of lncRNAs and diseases by principal component analysis, and to calculate the interaction profile for a new lncRNA by the interaction profiles. MFLDA [ 27 ] is a Matrix Factorization based LDAs prediction model that first encodes directly (or indirectly) relevant data sources related to lncRNAs or diseases in individual relational data matrices, and presets weights for these matrices. Then, it simultaneously optimizes the weights and low-rank matrix tri-factorization of each relational data matrix. RWSF-BLP , proposed in [ 28 ], applies a random walk-based multi-similarity fusion method to integrate different similarity matrices, mainly based on semantic and expression data, and bidirectional label propagation. The framework LRWRHLDA is proposed in [ 15 ] based on the construction of a global multi-layer network for LDAs prediction. First, four isomorphic networks including a lncRNA similarity network, a disease similarity network, a gene similarity network and a miRNA similarity network are constructed. Then, six heterogeneous networks involving known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations are built to design the multi-layer network. In [ 29 ] the LDAP-WMPS LDA prediction model is proposed, based on weight matrix and projection score. LDAP-WMPS consists on three steps: the first one computes the disease projection score; the second step calculates the lncRNA projection score; the third step fuses the disease projection score and the lncRNA projection score proportionally, then it normalizes them to get the prediction score matrix.

For most of the approaches summarized above, the performance is evaluated using the LOOCV framework, such that each known LDA is left out in turn as a test sample, and how well this test sample is ranked relative to the candidate samples (all the LDAs without the evidence to confirm their relationships) is computed.

The main goal of the research presented here is to provide more accurate computational methods for the prediction of novel LDAs, candidate for experimental validation in laboratory. To this aim, external information on both molecular interactions (e.g., lncRNA-miRNA interactions) and genotype-phenotype associations (e.g., miRNA-disease associations) is assumed to be available. Indeed, while only a restricted number of validated LDAs is yet available, large amounts of interactions between lncRNAs and other molecules (e.g., miRNAs, genes, proteins), as well as associations between these other molecules and diseases, are known and annotated in curated databases.

A commonly recognized assumption is that lncRNAs with similar behaviour in terms of their molecular interactions with other molecules, may also reflect such a similarity for their involvement in the occurrence and progress of disorders and diseases [ 12 ]. This is even more effective if the correlation with diseases is “mediated” by the molecules they interact with. Based on this observation, we have designed three novel prediction methods that all consider the notion of lncRNA “neighbors”, intended as lncRNAs which share common mediators among the molecules they physically interact with. Here, we focus on miRNAs as mediator molecules. However, the proposed approaches are general enough to allow also the inclusion of other different molecules. Relationships among lncRNAs, mediators and diseases are modeled through tripartite graphs in all the proposed approaches (see Fig.  1 that illustrates the flowchart of the presented research pipeline).

Problem statement Let \({\mathcal {L}}=\{l_1, l_2, \ldots , l_h\}\) be a set of lncRNAs and \({\mathcal {D}}=\{d_1, d_2, \ldots , d_k\}\) be a set of diseases. The goal is to return an ordered set of triplets \({\mathcal {R}}=\{\langle l_x, d_y, s_{xy}\rangle \}\) (with \(x\in [1,h]\) , and \(y\in [1,k]\) ), ranked according to the score \(s_{xy}\) .

The top triplets in \({\mathcal {R}}\) correspond to those pairs \((l_x, d_y)\) with most chances to represent putative LDAs which may be considered for further analysis in laboratory, while the triplets in the bottom correspond to lncRNAs and diseases which are unlikely to be related each other. A key aspect for the solution of the problem defined above is the score computation, that is the main aim of the approaches introduced in the following.

NGH: neighborhood based approach

A model of tripartite graph is adopted here to take into account that lncRNAs interacting with common mediators may be involved in common diseases.

Let \(T_{LMD}=\langle I, A \rangle\) be a tripartite graph defined on the three sets of disjoint vertexes L , M and D , such that \((l,m) \in I\) are edges between vertexes \(l \in L\) and \(m \in M\) , \((m,d) \in A\) are edges between vertexes \(m \in M\) and \(d \in D\) , respectively. In particular, L is associated to a set of lncRNAs, M to a set of miRNA and D to a set of diseases. Moreover, edges of the type ( l ,  m ) represent molecular interactions between lncRNAs and miRNA, experimentally validated in laboratory; edges of the type ( m ,  d ) correspond to known miRNA-disease associations, according to the existing literature. In both cases, interactions and associations annotated and stored in public databases may be taken into account.

The following definitions hold.

Definition 1

(Neighbors) Two lncRNAs \(l_h, l_k \in L\) are neighbors in \(T_{LMD}=\langle I, A \rangle\) if there exists at least a \(m_x \in M\) such that \((l_h, m_x) \in I\) and \((l_k, m_x) \in I\) .

Definition 2

(Prediction Score) The Prediction Score for the pair \((l_i,d_j)\) such that \(l_i \in L\) and \(d_j \in D\) is defined as:

\(M_{l_i}\) is the set of annotated miRNA interacting with \(l_i\) ,

\(M_{d_j}\) is the set of miRNA found to be associated to \(d_j\) ,

\(M_{l_x}\) is the set of miRNA interacting with the neighbor \(l_x\) of \(l_i\) (for each neighbor of \(l_i\) ),

\(\alpha\) is a real value in [0, 1] used to balance the two terms of the formula.

Definition 3

(Normalized prediction score) The Normalized Prediction Score for the pair \((l_i,d_j)\) such that \(l_i \in L\) , \(d_j \in D\) and \(s_{ij}\) is the Prediction Score for \((l_i,d_j)\) , is defined as:

NGH-CF: NGH extended with collaborative filtering

We remark that the main idea here is trying to infer the behaviour of a lncRNA, from that of its neighbors. Moreover, it is worth to point out that the notion of neighbor is related to the presence of miRNAs interacting with the same lncRNAs. However, not all the miRNA-lncRNA interactions have already been discovered, and miRNA-disease associations as well. This intuitively reminds to a typical context of data incompleteness where Collaborative Filtering may be successful in supporting the prediction process [ 30 ].

In more detail, what to be encoded by the Collaborative Filter is that lncRNAs presenting similar behaviours in terms of interactions with miRNAs, should reflect such a similarity also in their involvement with the occurrence and progress of diseases, mediated by those miRNAs. To this aim, a matrix R is considered here such that each element \(r_{ij}\) represents if (or to what extent) the lncRNA i and the disease j may be considered related. We call R relationship matrix (it is also known as rating matrix in other contexts, such as for example in the prediction of user-item associations). How to obtain \(r_{ij}\) is at the basis of the two variants of the approach presented in this section.

Due to the fact that R is usually a very sparse matrix, it can be factored into other two matrices L and D such that R \(\approx\) \(L\) \(^T\) \(D\) . In particular, matrix factorization models map both lncRNAs and diseases to a joint latent factor space F of dimensionality f , such that each lncRNA i is associated with a vector \(l_i \in F\) , each disease j with a vector \(d_j \in F\) , and their relationships are modeled as inner products in that space. Indeed, for each lncRNA i , the elements of \(l_i\) measure the extent to which it possesses those latent factors, and the same holds for each disease j and the corresponding elements of \(d_j\) . The resulting dot product in the factor space captures the affinity between lncRNA i and disease j , with reference to the considered latent factors. To this aim, there are two important tasks to be solved:

Mapping lncRNAs and diseases into the corresponding latent factors vectors.

Fill the matrix R , that is, the training set.

To learn the factor vectors \(l_i\) and \(d_j\) , a possible choice is to minimize the regularized squared error on the set of known relationships:

where \(\chi\) is the set of ( i ,  j ) pairs for which \(r_{ij}\) is not equal to zero in the matrix R . To this aim, we apply the ALS technique [ 31 ], which rotates between fixing the \(l_i\) ’s and fixing the \(d_j\) ’s. When all \(l_i\) ’s are fixed, the system recomputes the \(d_j\) ’s by solving a least-squares problem, and vice versa.

Filling the matrix R is performed according to two different criteria, resulting in the two different variants of the approach presented in this section, namely, CF and NGH-CF, respectively. According to the first criteria (CF), \(r_{ij}\) is set equal to 1 if the lncRNA i and the disease j share at least one miRNA in common, to 0 otherwise. The second variant (NGH-CF) works instead as a booster to improve the accuracy of NGH. In this latter case, the matrix R is filled by the normalized score ( 2 ). For both variants, the considered score to rank the predicted LDAs is given by the final value returned by the ALS technique applied on the corresponding matrix R .

Validation methodologies

We remark that the proposed approaches for LDAs prediction return a rank of LDAs, sorted according to the score that is characteristic of the considered approach, such that top triplets may be assumed as the most promising putative LDAs for further analysis in laboratory. As in other contexts [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], the performance of a prediction tool may be evaluated using suitable external criteria . Here, an external criterion relies on the existence of LDAs that are known to be true from the literature or, even better, from public repositories, where associations already verified in laboratory are annotated. A gold standard is constructed, containing only such true LDAs. The putative LDAs returned by the prediction method can thus be compared against those in the gold standard. In order to work properly, this validation methodology requires the gold standard information to be independent on that considered, in its turn, from the method under evaluation during its prediction task. This is satisfied in our case, due to the fact that all three approaches introduced in the previous sections do not exploit any type of knowledge referred to known LDAs during prediction, relying instead on known miRNA-lncRNA interactions and miRNA-disease associations, which come from independent sources.

According to the above mentioned validation methodology, the proposed approaches can be validated with references to the Receiver Operating Characteristics (ROC) analysis [ 34 ]. In particular, each predicted LDA is associated to a label, that is true if that association is contained in the considered gold standard, and false otherwise.

By varying the threshold value, it is possible to compute the true positive rate (TPR) and the false positive rate (FPR), by refferring to the percentage of the true/false predictions whose ranking is higher/below than the considered threshold value. ROC curve can be drawn by plotting TPR versus FPR at different threshold values. The Area Under ROC Curve (ROC-AUC) is further calculated to evaluate the performance of the tested methods. ROC-AUC equal to 1 indicates perfect performance, ROC-AUC equal to 0.5 random performance.

Similarly to the ROC curve, the Precision-Recall (PR) curve can be drawn as well, combining the positive predictive value (PPV, Precision), i.e., the fraction of predicted LDAs which are true in the gold standard, and the TPR (Recall), in a single visualization, at the threshold varying. The higher on y-axis the obtained curve is, the better the prediction method performance. The Area Under PR curve (AUPR) is more sensitive than AUC to the improvements for the positive class prediction [ 35 ], that is important for the case studied here. Indeed, only true LDAs are known, therefore no negative samples are included in the gold standard.

Another important measure useful to evaluate the prediction accuracy of a method and that can be considered here is the F1-score, defined as the harmonic mean of Precision and Recall to symmetrically represent both metrics in a single one.

We have validated the proposed approaches on both syntetic and real datasets, as explained below.

Synthetic data

A synthetic dataset has been built with 15 lncRNAs, 35 miRNA and 10 diseases, such that three different sets of LDAs may be identified, as follows (see also Table 1 , where the characteristics of each LDA are summarized).

Set 1: 26 LDAs, such that each lncRNA has from 3 to 4 miRNAs shared with the same disease (strongly linked lncRNAs) .

Set 2: 16 LDAs, each lncRNA having only one miRNA shared with a disease, and from 2 to 5 neighbors that are strongly linked with that same disease (directly linked lncRNAs and strong neighborhood) .

Set 3: 12 LDAs involving lncRNAs without any miRNA in common with a certain disease, and a number between 2 and 5 neighbors that are strongly linked with that same disease (only strong neighborhood) .

Experimentally verified data downloaded from starBase [ 36 ] and from HMDD [ 37 ] have been considered for the lncRNA-miRNA interactions and for the miRNA-disease associations, respectively. In particular, the latest version of HMDD, updated at 2019, has been used. Overall, \(1,\!114\) lncRNAs, \(1,\!058\) miRNAs, 885 diseases, \(10,\!112\) lncRNA-miRNA interactions and \(16,\!904\) miRNA-disease associations have been included in the analysis.

In order to evaluate the prediction accuracy of the approaches proposed here against those from the literature, three different gold standards have been considered. A first gold standard dataset GS1 has been obtained from the LncRNA-Disease database [ 38 ], resulting in 183 known and verified LDAs. A second, more restrictive, gold standard GS2 with 157 LDAs has been built by the intersection of data from [ 38 ] and [ 39 ]. Finally, also a larger gold standard dataset GS3 has been included in the analysis, by extracting LDAs from MNDRv2.0 database [ 40 ], where associations both experimentally verified and retrieved from manual literature curation are stored, resulting in 408 known LDAs.

Comparison on real data

The approaches proposed here have been compared against other approaches from the literature, over the three different gold standards described in the previous Section. In particular, all approaches considered from the literature have been run according to the default setting of their parameters, reported on the corresponding scientific publications and/or on their manual instructions.

Our approaches have been compared at first on GS1 against those approaches taking exactly the same input than ours, that are HGLDA [ 11 ], ncPred [ 10 ] and LDA-TG [ 24 ]. In particular, we have implemented HGLDA and used the corresponding p-value score, corrected by FDR as suggested by [ 11 ], for the ROC analysis. Moreover, we have normalized also the scores returned by ncPred and LDA-TG for the predicted LDAs, according to the formula in Definition 3 . Indeed, we have observed experimentally that such a normalization improves the accuracy of both methods from the literature, resulting in a better AUC. As for the novel approaches proposed here, the Normalized Prediction Score has been considered for NGH, while the approximated rating score resulting from ALS [ 31 ] is used for both CF and NGH-CF. Figure  2 shows the AUC scored by each method on GS1, while in Fig.  3 the different ROC curves are plotted. In particular, NGH scores a value of AUC equal to 0.914, thus outperforming the other three methods previously presented in the literature, i.e., HGLDA, ncPred and LDA-TG, that reach 0.876, 0.886 and 0.866, respectively (we remark also that performance of both ncPred and LDA-TG has been slightly improved with respect to their original one, by normalizing their scores). As for the novel approaches based on collaborative filtering, they both present a better accuracy than the others, with CF having AUC equal to 0.957 and NGH-CF to 0.966, respectively. Therefore, these results confirm that taking into account the collaborative effects of lncRNAs and miRNAs is useful to improve LDAs prediction, and the most successful approach is NGH-CF, that is, the neighborhood based approach boosted by collaborative filtering.

Comparison of the scored AUC on GS1

ROC curves for the compared methods on GS1

Another interesting issue is represented by the “agreement” between the different methods taking the same input, in terms of the returned best scoring LDAs. Table 2 shows the Jaccard Index computed between the proposed approaches and those receiving the same input, on the top \(5\%\) LDAs in the corresponding ranks, sorted from the best to the worst score values for each method. It emerges that results by HGLDA and ncPred have a small match with the other approaches (at most 0.23), while NGH-CF has high agreement with CF (0.74), as well as with NGH and LDA-TG (both 0.70). LDA-TG and CF present a sufficient match in their best predictions (0.59). This latter comparison based on agreement shows that approaches based on neighborhood analysis share a larger set of LDAs, in the top part of their ranks.

The proposed approaches have been compared also against other two recent methods from the literature, i.e., SIMCLDA and HGNNLDA, which receive in input different data than ours, including mRNA and drugs. For this reason, the more restrictive gold standard GS2 has been exploited for the comparison, where only lncRNAs and diseases having some correspondences with the additional input data of SIMCLDA and HGNNLDA are included. Figure  4 shows the comparison of the scored AUC on GS2, while Fig.  5 the corresponding ROC curves. In particular, the behaviour of all approaches previously tested does not change significantly on this other gold standard, moreover all the other approaches overcome SIMCLDA. On the other hand, HGNNLDA has a better performance than HGLDA, NcPred and LDA-TG, although it has a worse accuracy than NGH, CF and NGH-CF. The former confirms its superiority with regards to all considered approaches.

Comparison of the scored AUC on GS2

ROC curves for the compared methods on GS2

The proposed approaches have been compared also against LDAP-WMPS on GS3. Figure  6 shows the AUC values scored by all compared approaches on GS3, while Fig.  7 the corresponding ROC curves. In particular, the behaviour of all approaches previously tested does not change on this other gold standard, and LDAP-WMPS has better performance than the other approaches except for NGH, CF, NGH-CF and HGNNLDA.

Comparison of the scored AUC on GS3

ROC curves for the compared methods on GS3

The AUPR values scored by the compared methods on GS1, GS2, and GS3 are shown in Fig.  8 , while the corresponding PR-curves are plotted in Fig.  9 . In particular, for GS1 results are analogous to the ROC analysis, with NGH-CF the best performing one, followed by CF and NGH, while HGLDA is the worst. On GS2, NGH-CF and CF keep their superiority, followed by SMCLDA and NGH, while HGLDA is yet the worst one. On GS3, NGH-CF is the first, Cf the second and both HGNNLDA and LDAP-WMPS outperform NGH, while HGLDA in this case slightly outperforms LDA-TG, ncPred and SMCLDA, which results to be the worst one.

AUPR hystogram for the compared methods on GS1, GS2, GS3

Precision-recall curves for the compared methods on GS1,GS2,GS3

Figures 10 , 11 and 12 show the F1-score values obtained, for all methods compared on GS1, GS2 and GS3, respectively, at the varying of a threshold fixed on the method score. In Tables 3 , 4 and 5 it is shown, for each gold standard, the highest value of F1-score obtained by each considered method, as well as the corresponding Precision and Recall values, and the minimum threshold value for which the highest F1-score value has been reached. On GS1 and GS2, the three best performing approaches are NGH-CF, CF and NGH, in this order. On GS3 the order is the same, and LDAP-WMPS performs equally to NGH.

F1-score for the compared methods on GS1

F1-Score for the compared methods on GS2

F1-Score for the compared methods on GS3

Robustness analysis

The main aim of the analysis discussed here is to measure to what extent the proposed methods are able to correctly recognize verified LDAs, even if part of the existing associations are missed, i.e., the sets of known and verified lncRNA-miRNA interactions and miRNA-disease associations are not complete. This is important to verify that the proposed approaches can provide reliable predictions also in presence of data incompleteness, that is often the case when lncRNAs are involved. Therefore, the robustness of each proposed method has been evaluated by performing progressive alterations of the input associations coming from the real datasets, according to the following three different criteria.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of lncRNA-miRNA interactions from the input data.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of miRNA-disease associations from the input data.

Progressively eliminate the \(5\%\) , \(10\%\) , \(15\%\) and \(20\%\) of both lncRNA-miRNA interactions and miRNA-disease associations (half and half), from the input data.

Tests summarized above have been performed for 20 times each. Tables 6 , 7 and 8 show the mean of the AUC values for NGH, CF and NGH-CF, respectively, over the 20 tests. In particular, all methods perform well on the three test typologies at \(5\%\) , the worst being NGH-CF, which however presents an average AUC equal to 0.84 for case 1), that is still a high value. NGH-CF is also the method that presents the best robustness on case 3), keeping the value of 0.92 also at \(20\%\) , while CF is the worst performing in case 3), indeed its average AUC decreases from 0.95 at \(5\%\) to 0.63 already at \(10\%\) , and then to 0.50 at \(20\%\) . This behaviour in case 3), where both lncRNA-miRNA interactions and miRNA-disease associations are progressively eliminated, deserves some observations. Indeed, results show that the combination of neighborhood analysis and collaborative filtering is the most robust one with regards to this perturbation, while collaborative filtering alone is the worst performing. On the other hand, CF results to be the most robust in case 1), where only lncRNA-miRNA interactions are eliminated, and this is due to the fact that CF does not take into account how many miRNAs are shared by pairs of lncRNAs. As for case 2), performance of all methods is comparable and generally good, possibly in consideration of the fact that a large number of miRNA-disease associations are available, therefore discarding small percentages of them does not affect largely the final prediction.

Comparison on specific situations

In this section further experimental tests are described, showing how well the considered methods perform in detecting specific situations, depicted through the synthetic dataset first, and then searched for in the real data. In particular, the basic observation here is that prediction approaches from the literature usually fail in detecting true LDAs, when the involved lncRNAs and diseases do not have a large number of shared miRNAs (referring to those approaches taking the same input than ours). The novel approaches we propose are particularly effective in managing the situation depicted above, through neighborhood analysis and collaborative filtering, allowing to detect similar behaviours shared by different lncRNAs, depending on the miRNAs they interact with.

For each set of LDAs defined in the synthetic data (i.e., set 1, set 2, and set 3), and for each tested method (i.e., HGLDA, NCPRED, NHG, CF, NGH-CF), Table 9 shows the percentage of LDAs in that set which is recognized at the top \(10\%\) , \(20\%\) , \(30\%\) , \(50\%\) of the rank of all LDAs, sorted by the score returned by the considered method. As an example, for HGLDA the \(32\%\) of LDAs of set 1 are located in the top \(10\%\) of its rank, where instead none LDAs in set 2 or 3 find place.

Looking at these results some interesting considerations come out. First of all, for the methods HGLDA, NCPRED, NHG and CF most associations of the set 1 are located in the top \(50\%\) of their corresponding ranks, while NGH-CF has a different behaviour. Indeed, it locates a lower number of such LDAs in the highest part of its rank than the other approaches, possibly due to the fact that it leaves room for a larger number of associations in the other two sets in the top ranked positions. As for LDAs in the set 2, all methods recognize some of them already in the top \(10\%\) , except for HGLDA, as alredy highlighted. The approaches able to recognize the larger percentages of these associations at the top \(50\%\) of their rank are NGH and NGH-CF. LDAs in the set 3 are the most difficult to recognize, due to the fact that the lncRNA and the disease do not share any miRNA in common. Indeed, the worst performing methods in this case are HGLDA, which is able to locate some of these associations only at the top \(50\%\) (according to the percentages we considered here), and NCPRED, which performs slightly better although it reaches the same percentage of located associations than HGLDA at \(50\%\) (the \(28\%\) ). As expected, approaches based on neighborhood analysis and collaborative filtering perform better, with the best one resulting to be NGH-CF.

In the previous section we have shown that all methods proposed here are able to detect specific situations, characterized by the fact that a lncRNA may have very few (or none) common miRNAs with a disease, and its neighbors share instead a large set of miRNAs with that disease. We have checked if this case occurs among the verified LDAs that our approaches find and their competitors do not. Table 10 shows, only by meaning of example, 10 experimentally verified LDAs, included in GS1, that are top ranked for the novel approaches proposed here, whereas they are in the bottom rank of the other approaches from the literature compared on GS1. Six out of such LDAs do not present any common miRNAs between the lncRNA and the disease, while four share only one miRNA. All involved lncRNAs present neighbors with a large number of miRNAs in common with the disease in that LDA, in accordance with the hypothesis that the ability in capturing this situation allows to obtain a better accuracy.

Survival analysis has been also performed by one of the TCGA Computational Tools, that is, TANRIC [ 41 ], on four of the pairs in Table 10 . In particular, those lncRNAs and diseases available in TANRIC have been chosen. Results are reported in Figures 13 , 14 , 15 and 16 , showing that the over-expression of the considered lncRNA determines a lower survival probability over the time, for all four considered cases.

Survival analysis related to SNHG16 and bladder neoplasm

Survival analysis related to CBR3-AS1 and prostate neoplasm

Survival analysis related to MALAT1 and bladder neoplasm

Survival analysis related to MEG3 and breast neoplasm

In the previous sections the effectiveness and robustness of the proposed approaches have been illustrated, showing that all three are able to return reliable predictions, as well as to detect specific situations which may occur in true predictions and are missed by competitors. Here we provide a discussion on some novel LDAs predicted by NGH, CF and NGH-CF.

Table 11 shows seven LDAs which are not present in the considered gold standards, and that have been returned by all three methods proposed here, with highest score. The first of these associations is between CDKN2B-AS1 and LEUKEMIA, confirmed by recent literature [ 42 , 43 ]. Indeed, CDKN2B-AS1 was found to be highly expressed in pediatric T-ALL peripheral blood mononuclear cells [ 42 ], moreover genome-wide association studies show that it is associated to Chronic Lymphocytic Leukaemia risk in Europeans [ 43 ]. As for the second association between DLEU2 and LEUKEMIA, DLEU2 is a long non-coding transcript with several splice variants, which has been identified by [ 44 ] through a comprehensive sequencing of a commonly deleted region in leukemia (i.e., the 13q14 region). Different investigations reported up regulation of this lncRNA in several types of cancers. The lncRNA H19 regulates GLIOMA angiogenesis [ 45 , 46 ], while MAP3K14 is one of the well-recognized biomarkers in the prognosis of renal cancer, which is reminiscent of the pancreatic metastasis from renal cell carcinoma [ 47 ]. MEG3 has been recently found to be important for the prediction of LEUKEMIA risk [ 48 ]. Multiple studies have shown that MIR155HG is highly expressed in diffuse large B-cell (DLBC) lymphoma and primary mediastinal B-cell lymphoma, and in chronic lymphocytic leukemia. The transcription factor MYB activates MIR155HG activity, which causes the epigenetic state of MIR155HG to be dysregulated and causes an abnormal increase in MIR155 [ 49 ]. Also the last top-ranked association in Table 11 between TUG1 and NON-SMALL CELL LUNG CARCINOMA has found evidence in the literature [ 50 , 51 , 52 ].

Tables 12 , 13 , and 14 show the top 100 (sorted by the scores returned by each method) novel LDA predictions that NGH and CF, NGH and NGH-CF, CF and NGH-CF have in common, respectively. Many of the lncRNAs involved in such top-ranked LDAs are not yet characterized in the literature, therefore results presented here may be considered a first attempt to provide novel knowledge about them, through their inferred association with known diseases.

We have explored the application of neighborhood analysis, combined with collaborative filtering, for the improvement of LDAs prediction accuracy. The three approaches proposed here have been evaluated and compared first against their direct competitors from the literature, i.e., the other methods which also use lncRNA-miRNA interactions and miRNA-disease associations, without exploiting a priori known LDAs. It results that all methods proposed here are able to outperform direct competitors, the best one (NGH-CF) also significantly (AUC equal to 0.966 against the 0.886 by NCPRED). In particular, it has been shown that the improvement in accuracy is due to the fact that our approaches capture specific situations neglected by competitors, relying on similar lncRNAs behaviour in terms of their interactions with the considered intermediate molecules (i.e., miRNAs). The proposed approaches have been then compared also against other recent methods, taking different inputs (e.g., integrative approaches), and the experimental evaluation shows that they are able to outperform them as well.

It is worth pointing out the importance of providing reliable data in input to the LDAs prediction approaches. As discussed in this manuscript, information on the lncRNAs relationships with other molecules, and between intermediate molecules and diseases, is provided in input to the proposed approaches. Reliable datasets have been used to perform the experimental analysis provided here. However, as the user may provide also different input datasets, it is important to point out that the reliability of the obtained predictions strictly depends on that of input information.

As neighborhood analysis has resulted to be effective in characterizing lncRNAs with regards to their association with known diseases, we plan to apply it also for predicting possible common functions among lncRNAs, for example by clustering them according to their interactions, which has shown to be successful for other types of molecules [ 53 ]. Moreover, due to the success of integrative approaches on the analysis of biological data [ 54 ], we expect that including other types of intermediate molecules, such as for example genes and proteins, in the main pipeline of the proposed approaches may further improve their accuracy.

In conclusion, the use of reliable input data and the integration of different types of information coming from molecular interactions seem to be the most promising future directions for LDAs prediction.

Availability of data and materials

The source code is available at: https://github.com/marybonomo/LDAsPredictionApproaches.git In particular, executable software for NGH, CF, and NGH-CF are provided, as well as syntetic and real input datasets used here; the three different gold standard datasets GS1, GS2, GS3; the final obtained results.

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Acknowledgements

The authors are grateful to the Anonymous Reviewers, for the constructive and useful suggestions that allowed to significantly improve the quality of this manuscript. Some of the results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga .

PRIN “multicriteria data structures and algorithms: from compressed to learned indexes, and beyond”, Grant No. 2017WR7SHH, funded by MIUR (closed). “Modelling and analysis of big knowledge graphs for web and medical problem solving” (CUP: E55F22000270001), “Computational Approaches for Decision Support in Precision Medicine” (CUP:E53C22001930001), and “Knowledge graphs e altre rappresentazioni compatte della conoscenza per l’analisi di big data” (CUP: E53C23001670001), funded by INdAM GNCS 2022, 2023, 2024 projects, respectively. “Models and Algorithms relying on knowledge Graphs for sustainable Development goals monitoring and Accomplishment - MAGDA” (CUP: B77G24000050001), funded by the European Union under the PNRR program related to “Future Artificial Intelligence - FAIR”.

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Mariella Bonomo & Simona E. Rombo

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MB and SER equally contributed to the research presented in this manuscript. MB implemented and run the software, SER performed the analysis of results. Both authors wrote and reviewed the entire manuscript.

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Bonomo, M., Rombo, S.E. Neighborhood based computational approaches for the prediction of lncRNA-disease associations. BMC Bioinformatics 25 , 187 (2024). https://doi.org/10.1186/s12859-024-05777-8

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Received : 13 December 2023

Accepted : 11 April 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s12859-024-05777-8

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