trending research topics in biology

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Biology News

Biology is the scientific study of life and living organisms, encompassing various sub-disciplines such as microbiology, botany, zoology, and physiology. We’re dedicated to bringing you the latest research findings, innovative technologies, and thought-provoking discoveries from top scientists, research institutions, and universities around the world.

This section on biology news includes new research related to many related subjects such as biochemistry, genetics, cytology, and microbiology. Popular sub-topics include Biotechnology , DNA , Microbiology , Neurology , Evolutionary Biology , Genetics , Stem Cells , Neuroscience , Bioengineering , and Cell Biology .

Whether you are a professional biologist, an aspiring scientist, or simply someone with a passion for learning about the living world, our Biology News page offers a wealth of information and insights to keep you informed and inspired.

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Biology May 28, 2024

The Frequencies of Cognition: Exploring How Our Brains Differentiate Sounds

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Scientists Discover Unusual Ancient Fish 900 Miles North of Its Previous Known Range

Scientists Uncover Key Brain Development Differences in Autistic Boys and Girls

Scientists Pinpoint Main Cause of Sensory Hypersensitivity in Autism

Common Ancestor Identified – Researchers Decode the Ancient Origins of Horns, Antlers, and Ossicones

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Dartmouth Researchers Have Mapped How the Brain Regulates Emotions

Biology May 25, 2024

Simple New Strategy Improves Safety and Precision of CRISPR Gene Editing

KAUST researchers have improved CRISPR gene editing safety by reducing harmful DNA deletions and enhancing repair mechanisms, advancing towards safer genetic treatments. A simple and…

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Surprising Scientists: Eurasian Jays Exhibit Human-Like Memory Traits

A study has found that jays can remember incidental details, which is similar to episodic memory in humans. According to a study recently published in…

Biology May 24, 2024

Scripps Scientists Pioneer Vaccine To Combat Deadly “Zombie Drug” Xylazine

Chemical biologists at Scripps Research have developed a preliminary “proof-of-concept” vaccine, potentially paving the way for the first effective treatment for xylazine overdose in humans….

Echoes of Evolution: Unveiling Nature’s Repetitive Secrets in Stick Insects

Researchers present evidence of repeatable evolutionary patterns in California’s stick insect populations, demonstrating that while short-term evolution can be predictable due to constant environmental pressures…

Researchers Identify New Genetic Form of Alzheimer’s Disease

Scientists have discovered that nearly all individuals with two copies of the APOE4 gene will develop signs of Alzheimer’s disease, suggesting a distinct genetic form…

Biology May 23, 2024

Across the Entire Tree of Life – “Cell Cannibalism” Is Much More Common Than Scientists Thought

New research reveals that cell-in-cell phenomena, where one cell consumes another, are common across all life forms and important for normal biological functions, not just…

Sexual Parasitism: Yale Scientists Unveil the Mating Mysteries of Deep-Sea Anglerfish

Yale researchers have uncovered how sexual parasitism among deep-sea anglerfish, involving males permanently attaching to females, facilitated their adaptation from shallow-water habitats to the deep-sea…

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Researchers discovered a novel antibacterial mechanism of plectasin, an antibiotic derived from a fungus. The study reveals that plectasin forms Velcro-like structures that trap crucial…

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New Research Suggests That Your Gut Bacteria Could Impact Your Decision-Making

Research reveals that the gut microbiota affects mental health and social behavior, with studies showing that it can influence decision-making related to fairness, highlighting potential…

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Our biological science research journals cover a broad range of topics, from animal biology and plant science to genetic engineering, immunology, and microbiology. Read some of the latest and most talked about research from across our portfolio.

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Read the most mentioned* articles from each of our journals in 2023 so far.

The manifold costs of being a non-native English speaker in science , PLOS Biology

A novel nematode species from the Siberian permafrost shares adaptive mechanisms for cryptobiotic survival with C. elegans dauer larva , PLOS Genetics

Time-travelling pathogens and their risk to ecological communities , PLOS Computational Biology

The viral origin of myalgic encephalomyelitis/chronic fatigue syndrome , PLOS Pathogens

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From PLOS Biology , Eggs of the mosquito Aedes aegypti survive desiccation by rewiring their polyamine and lipid metabolism

From PLOS Computational Biology , Rapid automated 3-D pose estimation of larval zebrafish using a physical model-trained neural network

From PLOS Pathogens , Divergence of TORC1-mediated stress response leads to novel acquired stress resistance in a pathogenic yeast

From PLOS Genetics , Wolbachia infection at least partially rescues the fertility and ovary defects of several new Drosophila melanogaster bag of marbles protein-coding mutants

Read the latest multidisciplinary research from across our journals:

*based on data from Altmetric, accessed October 2023.

Biology Research Topics

Are you in need of captivating and achievable research topics within the field of biology? Your quest for the best biology topics ends right here as this article furnishes you with 100 distinctive and original concepts for biology research, laying the groundwork for your research endeavor.

Table of Contents

Our proficient researchers have thoughtfully curated these biology research themes, considering the substantial body of literature accessible and the prevailing gaps in research.

Should none of these topics elicit enthusiasm, our specialists are equally capable of proposing tailor-made research ideas in biology, finely tuned to cater to your requirements.

Thus, without further delay, we present our compilation of biology research topics crafted to accommodate students and researchers.

Research Topics in Marine Biology

Impact of climate change on coral reef ecosystems.
Biodiversity and adaptation of deep-sea organisms.
Effects of pollution on marine life and ecosystems.
Role of marine protected areas in conserving biodiversity.
Microplastics in marine environments: sources, impacts, and mitigation.

Biological Anthropology Research Topics

Evolutionary implications of early human migration patterns.
Genetic and environmental factors influencing human height variation.
Cultural evolution and its impact on human societies.
Paleoanthropological insights into human dietary adaptations.
Genetic diversity and population history of indigenous communities.

Biological Psychology Research Topics

Neurobiological basis of addiction and its treatment.
Impact of stress on brain structure and function.
Genetic and environmental influences on mental health disorders.
Neural mechanisms underlying emotions and emotional regulation.
Role of the gut-brain axis in psychological well-being.

Cancer Biology Research Topics

Targeted therapies in precision cancer medicine.
Tumor microenvironment and its influence on cancer progression.
Epigenetic modifications in cancer development and therapy.
Immune checkpoint inhibitors and their role in cancer immunotherapy.
Early detection and diagnosis strategies for various types of cancer.

Cell Biology Research Topics

Mechanisms of autophagy and its implications in health and disease.
Intracellular transport and organelle dynamics in cell function.
Role of cell signaling pathways in cellular response to external stimuli.
Cell cycle regulation and its relevance to cancer development.
Cellular mechanisms of apoptosis and programmed cell death.

Developmental Biology Research Topics

Genetic and molecular basis of limb development in vertebrates.
Evolution of embryonic development and its impact on morphological diversity.
Stem cell therapy and regenerative medicine approaches.
Mechanisms of organogenesis and tissue regeneration in animals.
Role of non-coding RNAs in developmental processes.

Human Biology Research Topics

Genetic factors influencing susceptibility to infectious diseases.
Human microbiome and its impact on health and disease.
Genetic basis of rare and common human diseases.
Genetic and environmental factors contributing to aging.
Impact of lifestyle and diet on human health and longevity.

Molecular Biology Research Topics

CRISPR-Cas gene editing technology and its applications.
Non-coding RNAs as regulators of gene expression.
Role of epigenetics in gene regulation and disease.
Mechanisms of DNA repair and genome stability.
Molecular basis of cellular metabolism and energy production.

Research Topics in Biology for Undergraduates

41. Investigating the effects of pollutants on local plant species.
Microbial diversity and ecosystem functioning in a specific habitat.
Understanding the genetics of antibiotic resistance in bacteria.
Impact of urbanization on bird populations and biodiversity.
Investigating the role of pheromones in insect communication.

Synthetic Biology Research Topics

Design and construction of synthetic biological circuits.
Synthetic biology applications in biofuel production.
Ethical considerations in synthetic biology research and applications.
Synthetic biology approaches to engineering novel enzymes.
Creating synthetic organisms with modified functions and capabilities.

Animal Biology Research Topics

Evolution of mating behaviors in animal species.
Genetic basis of color variation in butterfly wings.
Impact of habitat fragmentation on amphibian populations.
Behavior and communication in social insect colonies.
Adaptations of marine mammals to aquatic environments.

Best Biology Research Topics

Unraveling the mysteries of circadian rhythms in organisms.
Investigating the ecological significance of cryptic coloration.
Evolution of venomous animals and their prey.
The role of endosymbiosis in the evolution of eukaryotic cells.
Exploring the potential of extremophiles in biotechnology.

Biological Psychology Research Paper Topics

Neurobiological mechanisms underlying memory formation.
Impact of sleep disorders on cognitive function and mental health.
Biological basis of personality traits and behavior.
Neural correlates of emotions and emotional disorders.
Role of neuroplasticity in brain recovery after injury.

Biological Science Research Topics:

Role of gut microbiota in immune system development.
Molecular mechanisms of gene regulation during development.
Impact of climate change on insect population dynamics.
Genetic basis of neurodegenerative diseases like Alzheimer’s.
Evolutionary relationships among vertebrate species based on DNA analysis.

Biology Education Research Topics

Effectiveness of inquiry-based learning in biology classrooms.
Assessing the impact of virtual labs on student understanding of biology concepts.
Gender disparities in science education and strategies for closing the gap.
Role of outdoor education in enhancing students’ ecological awareness.
Integrating technology in biology education: challenges and opportunities.

Biology-Related Research Topics

The intersection of ecology and economics in conservation planning.
Molecular basis of antibiotic resistance in pathogenic bacteria.
Implications of genetic modification of crops for food security.
Evolutionary perspectives on cooperation and altruism in animal behavior.
Environmental impacts of genetically modified organisms (GMOs).

Biology Research Proposal Topics

Investigating the role of microRNAs in cancer progression.
Exploring the effects of pollution on aquatic biodiversity.
Developing a gene therapy approach for a genetic disorder.
Assessing the potential of natural compounds as anti-inflammatory agents.
Studying the molecular basis of cellular senescence and aging.

Biology Research Topic Ideas

Role of pheromones in insect mate selection and behavior.
Investigating the molecular basis of neurodevelopmental disorders.
Impact of climate change on plant-pollinator interactions.
Genetic diversity and conservation of endangered species.
Evolutionary patterns in mimicry and camouflage in organisms.

Biology Research Topics for Undergraduates

Effects of different fertilizers on plant growth and soil health.
Investigating the biodiversity of a local freshwater ecosystem.
Evolutionary origins of a specific animal adaptation.
Genetic diversity and disease susceptibility in human populations.
Role of specific genes in regulating the immune response.

Cell and Molecular Biology Research Topics

Molecular mechanisms of DNA replication and repair.
Role of microRNAs in post-transcriptional gene regulation.
Investigating the cell cycle and its control mechanisms.
Molecular basis of mitochondrial diseases and therapies.
Cellular responses to oxidative stress and their implications in ageing.

These topics cover a broad range of subjects within biology, offering plenty of options for research projects. Remember that you can further refine these topics based on your specific interests and research goals.

Frequently Asked Questions

What are some good research topics in biology?

A good research topic in biology will address a specific problem in any of the several areas of biology, such as marine biology, molecular biology, cellular biology, animal biology, or cancer biology.

A topic that enables you to investigate a problem in any area of biology will help you make a meaningful contribution.

How to choose a research topic in biology?

Choosing a research topic in biology is simple.

Follow the steps:

Generate potential topics.
Consider your areas of knowledge and personal passions.
Conduct a thorough review of existing literature.
Evaluate the practicality and viability.
Narrow down and refine your research query.
Remain receptive to new ideas and suggestions.

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For several years, Research Prospect has been offering students around the globe complimentary research topic suggestions. We aim to assist students in choosing a research topic that is both suitable and feasible for their project, leading to the attainment of their desired grades. Explore how our services, including research proposal writing , dissertation outline creation, and comprehensive thesis writing , can contribute to your college’s success.

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49 Most Interesting Biology Research Topics

August 21, 2023

In need of the perfect biology research topics—ideas that can both showcase your intellect and fuel your academic success? Lost in the boundless landscape of possible biology topics to research? And afraid you’ll never get a chance to begin writing your paper, let alone finish writing? Whether you’re a budding biologist hoping for a challenge or a novice seeking easy biology research topics to wade into, this blog offers curated and comprehensible options.

And if you’re a high school or transfer student looking for opportunities to immerse yourself in biology, consider learning more about research opportunities for high school students , top summer programs for high school students , best colleges for studying biomedical engineering , and best colleges for studying biology .

What is biology?

Well, biology explores the web of life that envelops our planet, from the teeny-tiny microbes to the big complex ecosystems. Biology investigates the molecular processes that define existence, deciphers the interplay of genes, and examines all the dynamic ways organisms interact with their environments. And through biology, you can gain not only knowledge, but a deeper appreciation for the interconnectedness of all living things. Pretty cool!

There are lots and lots of sub-disciplines within biology, branching out in all directions. Throughout this list, we won’t follow all of those branches, but we will follow many. And while none of these branches are truly simple or easy, some might be easier than others. Now we’ll take a look at a few various biology research topics and example questions that could pique your curiosity.

Climate change and ecosystems

The first of our potentially easy biology research topics: climate change and ecosystems. Investigate how ecosystems respond and adapt to the changing climate. And learn about shifts in species distributions , phenology , and ecological interactions .

1) How are different ecosystems responding to temperature changes and altered precipitation patterns?2) What are the implications of shifts in species distributions for ecosystem stability and functioning?

2) Or how does phenology change in response to climate shifts? And how do those changes impact species interactions?

3) Which underlying genetic and physiological mechanisms enable certain species to adapt to changing climate conditions?

4) And how do changing climate conditions affect species’ abilities to interact and form mutualistic relationships within ecosystems?

Microbiome and human health

Intrigued by the relationship between the gut and the rest of the body? Study the complex microbiome . You could learn how gut microbes influence digestion, immunity, and even mental health.

5) How do specific gut microbial communities impact nutrient absorption?

6) What are the connections between the gut microbiome, immune system development, and susceptibility to autoimmune diseases?

7) What ethical considerations need to be addressed when developing personalized microbiome-based therapies? And how can these therapies be safely and equitably integrated into clinical practice?

8) Or how do variations in the gut microbiome contribute to mental health conditions such as anxiety and depression?

9) How do changes in diet and lifestyle affect the composition and function of the gut microbiome? And what are the subsequent health implications?

Urban biodiversity conservation

Next, here’s another one of the potentially easy biology research topics. Examine the challenges and strategies for conserving biodiversity in urban environments. Consider the impact of urbanization on native species and ecosystem services. Then investigate the decline of pollinators and its implications for food security or ecosystem health.

10) How does urbanization influence the abundance and diversity of native plant and animal species in cities?

11) Or what are effective strategies for creating and maintaining green spaces that support urban biodiversity and ecosystem services?

12) How do different urban design and planning approaches impact the distribution of wildlife species and their interactions?

13) What are the best practices for engaging urban communities in biodiversity conservation efforts?

14) And how can urban agriculture and rooftop gardens contribute to urban biodiversity conservation while also addressing food security challenges?

Bioengineering

Are you a problem solver at heart? Then try approaching the intersection of engineering, biology, and medicine. Delve into the field of synthetic biology , where researchers engineer biological systems to create novel organisms with useful applications.

15) How can synthetic biology be harnessed to develop new, sustainable sources of biofuels from engineered microorganisms?

16) And what ethical considerations arise when creating genetically modified organisms for bioremediation purposes?

17) Can synthetic biology techniques be used to design plants that are more efficient at withdrawing carbon dioxide from the atmosphere?

18) How can bioengineering create organisms capable of producing valuable pharmaceutical compounds in a controlled and sustainable manner?

19) But what are the potential risks and benefits of using engineered organisms for large-scale environmental cleanup projects?

Neurobiology

Interested in learning more about what makes creatures tick? Then this might be one of your favorite biology topics to research. Explore the neural mechanisms that underlie complex behaviors in animals and humans. Shed light on topics like decision-making, social interactions, and addiction. And investigate how brain plasticity and neurogenesis help the brain adapt to learning, injury, and aging.

20) How does the brain’s reward circuitry influence decision-making processes in situations involving risk and reward?

21) What neural mechanisms underlie empathy and social interactions in both humans and animals?

22) Or how do changes in neural plasticity contribute to age-related cognitive decline and neurodegenerative diseases?

23) Can insights from neurobiology inform the development of more effective treatments for addiction and substance abuse?

24) What are the neural correlates of learning and memory? And how can our understanding of these processes be applied to educational strategies?

Plant epigenomics

While this might not be one of the easy biology research topics, it will appeal to plant enthusiasts. Explore how epigenetic modifications in plants affect their ability to respond and adapt to changing environmental conditions.

25) How do epigenetic modifications influence the expression of stress-related genes in plants exposed to temperature fluctuations?

26) Or what role do epigenetic changes play in plants’ abilities to acclimate to changing levels of air pollution?

27) Can certain epigenetic modifications be used as indicators of a plant’s adaptability to new environments?

28) How do epigenetic modifications contribute to the transgenerational inheritance of traits related to stress resistance?

29) And can targeted manipulation of epigenetic marks enhance crop plants’ ability to withstand changing environmental conditions?

Conservation genomics

Motivated to save the planet? Conservation genomics stands at the forefront of modern biology, merging the power of genetics with the urgent need to protect Earth’s biodiversity. Study genetic diversity, population dynamics, and how endangered species adapt in response to environmental changes.

30) How does genetic diversity within endangered species influence their ability to adapt to changing environmental conditions?

31) What genetic factors contribute to the susceptibility of certain populations to diseases, and how can this knowledge inform conservation strategies?

32) How can genomic data be used to inform captive breeding and reintroduction programs for endangered species?

33) And what are the genomic signatures of adaptation in response to human-induced environmental changes, such as habitat fragmentation and pollution?

34) Or how can genomics help identify “hotspots” of biodiversity that are particularly important for conservation efforts?

Zoonotic disease transmission

And here’s one of the biology research topics that’s been on all our minds in recent years. Investigate the factors contributing to the transmission of zoonotic diseases , like COVID-19. Then posit strategies for prevention and early detection.

35) What are the ecological and genetic factors that facilitate the spillover of zoonotic pathogens from animals to humans?

36) Or how do changes in land use, deforestation, and urbanization impact the risk of zoonotic disease emergence?

37) Can early detection and surveillance systems be developed to predict and mitigate the spread of zoonotic diseases?

38) How do social and cultural factors influence human behaviors that contribute to zoonotic disease transmission?

39) And can strategies be implemented to improve global pandemic preparedness?

Bioinformatics

Are you a data fanatic? Bioinformatics involves developing computational tools and techniques to analyze and interpret large biological datasets. This enables advancements in genomics, proteomics, and systems biology. So delve into the world of bioinformatics to learn how large-scale genomic and molecular data are revolutionizing biological research.

40) How can machine learning algorithms predict the function of genes based on their DNA sequences?

41) And what computational methods can identify potential drug targets by analyzing protein-protein interactions in large biological datasets?

42) Can bioinformatics tools be used to identify potential disease-causing mutations in human genomes and guide personalized medicine approaches?

43) What are the challenges and opportunities in analyzing “omics” data (genomics, proteomics, transcriptomics) to uncover novel biological insights?

44) Or how can bioinformatics contribute to our understanding of microbial diversity, evolution, and interactions within ecosystems?

Regenerative medicine

While definitely not one of the easy biology research topics, regenerative medicine will appeal to those interested in healthcare. Research innovative approaches to stimulate tissue and organ regeneration, using stem cells, tissue engineering, and biotechnology. And while you’re at it, discover the next potential medical breakthrough.

45) How can stem cells be directed to differentiate into specific cell types for tissue regeneration, and what factors influence this process?

46) Or what are the potential applications of 3D bioprinting in creating functional tissues and organs for transplantation?

47) How can bioengineered scaffolds enhance tissue regeneration and integration with host tissues?

48) What are the ethical considerations surrounding the use of stem cells and regenerative therapies in medical treatments?

49) And can regenerative medicine approaches be used to treat neurodegenerative disorders and restore brain function?

Biology Research Topics – Final thoughts

So as you take your next steps, try not to feel overwhelmed. And instead, appreciate the vast realm of possibilities that biology research topics offer. Because the array of biology topics to research is as diverse as the ecosystems it seeks to understand. And no matter if you’re only looking for easy biology research topics, or you’re itching to unravel the mysteries of plant-microbe interactions, your exploration will continue to deepen what we know of the world around us.

High School Success

Mariya holds a BFA in Creative Writing from the Pratt Institute and is currently pursuing an MFA in writing at the University of California Davis. Mariya serves as a teaching assistant in the English department at UC Davis. She previously served as an associate editor at Carve Magazine for two years, where she managed 60 fiction writers. She is the winner of the 2015 Stony Brook Fiction Prize, and her short stories have been published in Mid-American Review , Cutbank , Sonora Review , New Orleans Review , and The Collagist , among other magazines.

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200+ Unique And Interesting Biology Research Topics For Students In 2023

Are you curious about the fascinating world of biology and its many research possibilities? Well, you are in the right place! In this blog, we will explore biology research topics, exploring what biology is, what constitutes a good research topic, and how to go about selecting the perfect one for your academic journey.

So, what exactly is biology? Biology is the study of living organisms and their interactions with the environment. It includes everything from the tiniest cells to the largest ecosystems, making it a diverse and exciting field of study.

Stay tuned to learn more about biology research topics as we present over 200 intriguing research ideas for students, emphasizing the importance of selecting the right one. In addition, we will also share resources to make your quest for the perfect topic a breeze. Let’s embark on this scientific journey together!

If you are having trouble with any kind of assignment or task, do not worry—we can give you the best microbiology assignment help at a value price. Additionally, you may look at nursing project ideas .

What Is Biology?

Table of Contents

Biology is the study of living things, like animals, plants, and even tiny organisms too small to see. It helps us understand how these living things work and how they interact with each other and their environment. Biologists, or scientists who study biology, explore topics like how animals breathe, how plants grow, and how our bodies function. By learning about biology, we can better care for the Earth and all its living creatures.

What Is A Good Biology Research Topic?

A good biology research topic is a question or problem in the field of biology that scientists want to investigate and learn more about. It should be interesting and important, like studying how a new medicine can treat a disease or how animals adapt to changing environments. The topic should also be specific and clear, so researchers can focus on finding answers. Additionally, it’s helpful if the topic hasn’t been studied extensively before, so the research can contribute new knowledge to the field of biology and help us better understand the natural world.

Tips For Choosing A Biology Research Topics

Here are some tips for choosing a biology research topics:

1. Choose What Interests You

When picking a biology research topic, go for something that you personally find fascinating and enjoyable. When you’re genuinely curious about it, you’ll be more motivated to study and learn.

2. Select a Significant Topic

Look for a subject in biology that has real-world importance. Think about whether your research can address practical issues, like finding cures for diseases or understanding environmental problems. Research that can make a positive impact is usually a good choice.

3. Check If It’s Doable

Consider if you have the necessary tools and time to carry out your research. It’s essential to pick a topic that you can actually study with the resources available to you.

4. Add Your Unique Perspective

Try to find a fresh or different angle for your research. While you can build upon existing knowledge, bringing something new or unique to the table can make your research more exciting and valuable.

5. Seek Guidance

Don’t hesitate to ask for advice from your teachers or experienced researchers. They can provide you with valuable insights and help you make a smart decision when choosing your research topic in biology.

Biology Research Topics For College Students

1. Investigating the role of genetic mutations in cancer development.

2. Analyzing the impact of climate changes on wildlife populations.

3. Studying the ecology of invasive species in urban environments.

4. Investigating the microbiome of the human gut and its relationship to health.

5. Analyzing the genetic diversity of endangered species for conservation.

6. Studying the evolution of antibiotic resistance in bacteria.

7. Investigating the ecological consequences of deforestation.

8. Analyzing the behavior and communication of social insects like ants and bees.

9. Studying the physiology of extreme environments, such as deep-sea hydrothermal vents.

10. Investigating the molecular mechanisms of cell division and mitosis.

Plant Biology Research Topics For College Students

11. Studying the impact of different fertilizers on crop yields and soil health.

12. Analyzing the genetics of plant resistance to pests and diseases.

13. Investigating the role of plant hormones in growth and development.

14. Studying the adaptation of plants to drought conditions.

15. Analyzing the ecological interactions between plants and pollinators.

16. Investigating the use of biotechnology to enhance crop traits.

17. Studying the genetics of plant breeding for improved varieties.

18. Analyzing the physiology of photosynthesis and carbon fixation in plants.

19. Investigating the effects of soil microbiota on plant health.

20. Studying the evolution of plant species in response to changing environments.

Biotechnology Research Topics For College Students

21. Investigating the use of CRISPR-Cas9 technology for genome editing.

22. Analyzing the production of biofuels from microorganisms.

23. Studying the application of biotechnology in medicine, such as gene therapy.

24. Investigating the use of bioplastics as a sustainable alternative to conventional plastics.

25. Analyzing the role of biotechnology in food production, including GMOs.

26. Studying the development of biopharmaceuticals and monoclonal antibodies.

27. Investigating the use of bioremediation to clean up polluted environments.

28. Studying the potential of synthetic biology for creating novel organisms.

29. Analyzing the ethical and social implications of biotechnological advancements.

30. Investigating the use of biotechnology in forensic science, such as DNA analysis.

Molecular Biology Research Topics For Undergraduates

31. Studying the structure and function of DNA and RNA molecules.

32. Analyzing the regulation of gene expression in eukaryotic cells.

33. Investigating the mechanisms of DNA replication and repair.

34. Studying the role of non-coding RNAs in gene regulation.

35. Analyzing the molecular basis of genetic diseases like cystic fibrosis.

36. Investigating the epigenetic modifications that control gene activity.

37. Studying the molecular mechanisms of protein folding and misfolding.

38. Analyzing the molecular pathways involved in cancer progression.

39. Investigating the molecular basis of neurodegenerative diseases.

40. Studying the use of molecular markers in genetic diversity analysis.

Life Science Research Topics For High School Students

41. Investigating the effects of different diets on human health.

42. Analyzing the impact of exercise on cardiovascular fitness.

43. Studying the genetics of inherited traits and diseases.

44. Investigating the ecological interactions in a local ecosystem.

45. Analyzing the diversity of microorganisms in soil or water samples.

46. Studying the anatomy and physiology of a specific organ or system.

47. Investigating the life cycle of a local plant or animal species.

48. Studying the effects of environmental pollutants on aquatic organisms.

49. Analyzing the behavior of a specific animal species in its habitat.

50. Investigating the process of photosynthesis in plants.

Biology Research Topics For Grade 12

51. Investigating the genetic basis of a specific inherited disorder.

52. Analyzing the impact of climate change on a local ecosystem.

53.Studying the biodiversity of a particular rainforest region.

54. Investigating the physiological adaptations of animals to extreme temperatures.

55. Analyzing the effects of pollution on aquatic ecosystems.

56. Studying the life history and conservation status of an endangered species.

57. Investigating the molecular mechanisms of a specific disease.

58. Studying the ecological interactions within a coral reef ecosystem.

59. Analyzing the genetics of plant hybridization and speciation.

60. Investigating the behavior and communication of a particular bird species.

Marine Biology Research Topics

61. Studying the impact of ocean acidification on coral reefs.

62. Analyzing the migration patterns of marine mammals.

63. Investigating the physiology of deep-sea creatures under high pressure.

64. Studying the ecology of phytoplankton and their role in the marine food web.

65. Analyzing the behavior of different species of sharks.

66. Investigating the conservation of sea turtle populations.

67. Studying the biodiversity of deep-sea hydrothermal vent communities.

68. Analyzing the effects of overfishing on marine ecosystems.

69. Investigating the adaptation of marine organisms to extreme cold in polar regions.

70. Studying the bioluminescence and communication in marine organisms.

AP Biology Research Topics

71. Investigating the role of specific enzymes in cellular metabolism.

72. Analyzing the genetic variation within a population.

73. Studying the mechanisms of hormonal regulation in animals.

74. Investigating the principles of Mendelian genetics through trait analysis.

75. Analyzing the ecological succession in a local ecosystem.

76. Studying the physiology of the human circulatory system.

77. Investigating the molecular biology of a specific virus.

78. Studying the principles of natural selection through evolutionary simulations.

79. Analyzing the genetic diversity of a plant species in different habitats.

80. Investigating the effects of different environmental factors on plant growth.

Cell Biology Research Topics

81. Investigating the role of mitochondria in cellular energy production.

82. Analyzing the mechanisms of cell division and mitosis.

83. Studying the function of cell membrane proteins in signal transduction.

84. Investigating the cellular processes involved in apoptosis (cell death).

85. Analyzing the role of endoplasmic reticulum in protein synthesis and folding.

86. Studying the dynamics of the cytoskeleton and cell motility.

87. Investigating the regulation of cell cycle checkpoints.

88. Analyzing the structure and function of cellular organelles.

89. Studying the molecular mechanisms of DNA replication and repair.

90. Investigating the impact of cellular stress on cell health and function.

Human Biology Research Topics

91. Analyzing the genetic basis of inherited diseases in humans.

92. Investigating the physiological responses to exercise and physical activity.

93. Studying the hormonal regulation of the human reproductive system.

94. Analyzing the impact of nutrition on human health and metabolism.

95. Investigating the role of the immune system in disease prevention.

96. Studying the genetics of human evolution and migration.

97. Analyzing the neural mechanisms underlying human cognition and behavior.

98. Investigating the molecular basis of aging and age-related diseases.

99. Studying the impact of environmental toxins on human health.

100. Analyzing the genetics of organ transplantation and tissue compatibility.

Molecular Biology Research Topics

101. Investigating the role of microRNAs in gene regulation.

102. Analyzing the molecular basis of genetic disorders like cystic fibrosis.

103. Studying the epigenetic modifications that control gene expression.

104. Investigating the molecular mechanisms of RNA splicing.

105. Analyzing the role of telomeres in cellular aging.

106. Studying the molecular pathways involved in cancer metastasis.

107. Investigating the molecular basis of neurodegenerative diseases.

108. Studying the molecular interactions in protein-protein networks.

109. Analyzing the molecular mechanisms of DNA damage and repair.

110. Investigating the use of CRISPR-Cas9 for genome editing.

Animal Biology Research Topics

111. Studying the behavior and communication of social insects like ants.

112. Analyzing the physiology of hibernation in mammals.

113. Investigating the ecological interactions in a predator-prey relationship.

114. Studying the adaptations of animals to extreme environments.

115. Analyzing the genetics of inherited traits in animal populations.

116. Investigating the impact of climate change on animal migration patterns.

117. Studying the diversity of marine life in coral reef ecosystems.

118. Analyzing the physiology of flight in birds and bats.

119. Investigating the molecular basis of animal coloration and camouflage.

120. Studying the behavior and conservation of endangered species.

Neuroscience Research Topics
Mental Health Research Topics

Plant Biology Research Topics

121. Investigating the role of plant hormones in growth and development.

122. Analyzing the genetics of plant resistance to pests and diseases.

123. Climate change and plant phenology are being examined.

124. Investigating the ecology of mycorrhizal fungi and their symbiosis with plants.

125. Investigating plant photosynthesis and carbon fixing.

126. Molecular analysis of plant stress responses.

127. Investigating the adaptation of plants to drought conditions.

128. Studying the role of plants in phytoremediation of polluted environments.

129. Analyzing the genetics of plant hybridization and speciation.

130. Investigating the molecular basis of plant-microbe interactions.

Environmental Biology Research Topics

131. Analyzing the effects of pollution on aquatic ecosystems.

132. Investigating the biodiversity of a particular ecosystem.

133. Studying the ecological consequences of deforestation.

134. Analyzing the impact of climate change on wildlife populations.

135. Investigating the use of bioremediation to clean up polluted sites.

136. Studying the environmental factors influencing species distribution.

137. Analyzing the effects of habitat fragmentation on wildlife.

138. Investigating the ecology of invasive species in new environments.

139. Studying the conservation of endangered species and habitats.

140. Analyzing the interactions between humans and urban ecosystems.

Chemical Biology Research Topics

141. Investigating the design and synthesis of new drug compounds.

142. Analyzing the molecular mechanisms of enzyme catalysis.

143.Studying the role of small molecules in cellular signaling pathways.

144. Investigating the development of chemical probes for biological research.

145. Studying the chemistry of protein-ligand interactions.

146. Analyzing the use of chemical biology in cancer therapy.

147. Investigating the synthesis of bioactive natural products.

148. Studying the role of chemical compounds in microbial interactions.

149. Analyzing the chemistry of DNA-protein interactions.

150. Investigating the chemical basis of drug resistance in pathogens.

Medical Biology Research Topics

151. Investigating the genetic basis of specific diseases like diabetes.

152. Analyzing the mechanisms of drug resistance in bacteria.

153. Studying the molecular mechanisms of autoimmune diseases.

154. Investigating the development of personalized medicine approaches.

155. Studying the role of inflammation in chronic diseases.

156. Analyzing the genetics of rare diseases and genetic syndromes.

157. Investigating the molecular basis of viral infections and vaccines.

158. Studying the mechanisms of organ transplantation and rejection.

159. Analyzing the molecular diagnostics of cancer.

160. Investigating the biology of stem cells and regenerative medicine.

Evolutionary Biology Research Topics

161. Studying the evolution of human ancestors and early hominids.

162. The genetic variety of species and between species is being looked at.

163. Investigating the role of sexual selection in animal evolution.

164. Studying the co-evolutionary relationships between parasites and hosts.

165. Analyzing the evolutionary adaptations of extremophiles.

166. Investigating the evolution of developmental processes (evo-devo).

167. Studying the biogeography and distribution of species.

168. Analyzing the evolution of mimicry in animals and plants.

169. Investigating the genetics of speciation and hybridization.

170. Studying the evolutionary history of domesticated plants and animals.

Cellular Biology Research Topics

171. Investigating the role of autophagy in cellular homeostasis.

172. Analyzing the mechanisms of cellular transport and trafficking.

173. Studying the regulation of cell adhesion & migration.

174. Investigating the cellular responses to DNA damage.

175. Analyzing the dynamics of cellular membrane structures.

176. Studying the role of cellular organelles in lipid metabolism.

177. Investigating the molecular mechanisms of cell-cell communication.

178. Studying the physiology of cellular respiration and energy production.

179. Analyzing the cellular mechanisms of viral entry and replication.

180. Investigating the role of cellular senescence in aging and disease.

Good Biology Research Topics Related To Brain Injuries

181. Analyzing the molecular mechanisms of traumatic brain injury.

182. Investigating the role of neuroinflammation in brain injury recovery.

183. Studying the impact of concussions on long-term brain health.

184. Analyzing the use of neuroimaging in diagnosing brain injuries.

185. Investigating the development of neuroprotective therapies.

186. Studying the genetics of susceptibility to brain injuries.

187. Analyzing the cognitive and behavioral effects of brain trauma.

188. Investigating the role of rehabilitation in brain injury recovery.

189. Studying the cellular and molecular changes in axonal injury.

190. Looking into how stem cell therapy might be used to help brain injuries.

Biology Quantitative Research Topics

191. Investigating the mathematical modeling of population dynamics.

192. Analyzing the statistical methods for biodiversity assessment.

193. Studying the use of bioinformatics in genomics research.

194. Investigating the quantitative analysis of gene expression data.

195. Studying the mathematical modeling of enzyme kinetics.

196. Analyzing the statistical approaches for epidemiological studies.

197. Investigating the use of computational tools in phylogenetics.

198. Studying the mathematical modeling of ecological systems.

199. Analyzing the quantitative analysis of protein-protein interactions.

200. Investigating the statistical methods for analyzing genetic variation.

Importance Of Choosing The Right Biology Research Topics

Here are some importance of choosing the right biology research topics:

1. Relevance to Your Interests and Goals

Choosing the right biology research topic is important because it should align with your interests and goals. Studying something you’re passionate about keeps you motivated and dedicated to your research.

2. Contribution to Scientific Knowledge

Your research should contribute something valuable to the world of science. Picking the right topic means you have the chance to discover something new or solve a problem, advancing our understanding of the natural world.

3. Availability of Resources

Consider the resources you have or can access. If you pick a topic that demands resources you don’t have, your research may hit a dead end. Choosing wisely means you can work efficiently.

4. Feasibility and Manageability

A good research topic should be manageable within your time frame and capabilities. If it’s too broad or complex, you might get overwhelmed. Picking the right topic ensures your research is doable.

5. Real-World Impact

Think about how your research might benefit the real world. Biology often has implications for health, the environment, or society. Choosing a topic with practical applications can make your work meaningful and potentially change lives.

Resources For Finding Biology Research Topics

There are numerous resources for finding biology research topics:

1. Online Databases

Look on websites like PubMed and Google Scholar. They have lots of biology articles. Type words about what you like to find topics.

2. Academic Journals

Check biology magazines. They talk about new research. You can find ideas and see what’s important.

3. University Websites

Colleges show what their teachers study. Find teachers who like what you like. Ask them about ideas for your own study.

4. Science News and Magazines

Read science news. They tell you about new things in biology. It helps you think of research ideas.

5. Join Biology Forums and Communities

Talk to other people who like biology online. You can ask for ideas and find friends to help you. Use websites like ResearchGate and Reddit for this.

Conclusion

Biology Research Topics offer exciting opportunities for exploration and learning. We’ve explained what biology is and stressed the importance of picking a good research topic. Our tips and extensive list of over 200 biology research topics provide valuable guidance for students.

Selecting the right topic is more than just getting good grades; it’s about making meaningful contributions to our understanding of life. We’ve also shared resources to help you discover even more topics. So, embrace the world of biology research, embark on a journey of discovery, and be part of the ongoing effort to unravel the mysteries of the natural world.

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Top scientific breakthroughs and emerging trends for 2023

CAS Science Team

January 31, 2023

*Updated in January 2024*:

While the article below was created at the end of 2022, it still has critical insight and emerging trends for the future of scientific R&D. For the latest trends and breakthroughs, the scientists and experts at CAS recently published a new review on the landscape of scientific trends to watch in 2024: from AI, to emerging materials, our on-going battle against the undruggable proteins, sustainability trends, and more. Additionally, CAS teamed up with experts from Lawrence Livermore National Lab , Oak Ridge National Lab , and The Ohio State University , to reveal the top trends to watch in the year ahead. If you weren't able to join us for the webinar, see the recording here for the expert's take on the year ahead.

As published in 2022:

The pace of innovation never slows, and the impact of these scientific breakthroughs will redefine the way we live, work, and connect with the world around us. From space exploration at the largest scale to diagnostics at the single-cell level, these breakthroughs will inspire innovators to push the boundaries of what is possible.

Looking to stay ahead of emerging trends?

Subscribe to be the first to know when new insights are published.

A new era of space exploration

Need to be reminded of how incredibly vast our universe is? The first ever photos from the James Webb Space Telescope are awe-inspiring. While this is the most technically advanced and powerful telescope ever created, the learnings about our universe will lead to future missions and exploration for generations ahead. Recently, the newest mission to the moon was launched as NASA’s Artemis Program which will pave the way for a future mission to Mars. This new era of space exploration will drive technological advancements in fields beyond astronautics and stimulate progress in real-world applications like materials, food science , agriculture, and even cosmetics.

A milestone in AI predictions

For decades, the scientific community has chased a greater understanding of relationships between protein functions and 3D structures. In July 2022, Deep Mind revealed that the folded 3D structure of a protein molecule can be predicted from its linear amino-acid sequence using AlphaFold2 , RoseTTAFold , and trRosettaX-Single algorithms. The algorithms’ predictions reduced the number of human proteins with unknown structural data from 4,800 to just 29. While there will always be challenges with AI, the ability to predict protein structures has implications across all life sciences. Key challenges in the future include modeling proteins with intrinsic disordered properties and those that change structures by post-translational modifications or to environmental conditions. Beyond protein modeling, AI advancements continue to reshape workflows and expand discovery capabilities across many industries and disciplines .

Developing trends in synthetic biology

Synthetic biology has the potential to redefine synthetic pathways by using engineered biological systems (i.e., microorganisms, for which a large part of the genome or the entire genome has been designed or engineered) to manufacture a range of biomolecules and materials, such as therapeutics, flavors, fabrics, food, and fuels. For example, insulin could be produced without pig pancreas, leather without cows, and spider silk without spiders. The potential in life sciences alone is unbelievable, but when applied to manufacturing industries, synthetic biology could minimize future supply chain challenges, increase efficiency, and create new opportunities for biopolymers or alternative materials with more sustainable approaches. Today, teams use AI-based metabolic modeling, CRISPR tools, and synthetic genetic circuits to control metabolism, manipulate gene expression, and build pathways for bioproduction. As this discipline begins to cross over into multiple industries, the latest developments and emerging trends for metabolic control and engineering challenges are showcased in a 2022 Journal of Biotechnology article .

Single-cell metabolomics set to soar

While much progress has been made in genetic sequencing and mapping, genomics only tells us what a cell is capable of. To have a better understanding of cellular functions, proteomic and metabolomic approaches offer different angles for revealing molecular profiles and cellular pathways. Single-cell metabolomics gives a snapshot of the cellular metabolism within a biological system. The challenge is that metabolomes change rapidly, and sample preparation is critical to understand cell function. Collectively, a series of recent advancements in single-cell metabolomics (from open-sourced techniques, advanced AI algorithms, sample preparations, and new forms of mass spectrometry) demonstrates the ability to run detailed mass spectral analyses. This allows researchers to determine the metabolite population on a cell-by-cell basis, which would unlock enormous potential for diagnostics. In the future, this could lead to the ability to detect even a single cancerous cell in an organism. Combined with new biomarker detection methods , wearable medical devices and AI- assisted data analysis, this array of technologies will improve diagnosis and lives.

New catalysts enable greener fertilizer production

Every year, billions of people depend on fertilizers for the ongoing production of food, and reducing the carbon footprint and expenses in fertilizer production would reshape the impact agriculture has on emissions. The Haber-Bosch process for fertilizer production converts nitrogen and hydrogen to ammonia. To reduce energy requirements, researchers from Tokyo Tech have developed a noble-metal-free nitride catalyst containing a catalytically active transition metal (Ni) on a lanthanum nitride support that is stable in the presence of moisture. Since the catalyst doesn't contain ruthenium, it presents an inexpensive option for reducing the carbon footprint of ammonia production. The La-Al-N support, along with the active metals, such as nickel and cobalt (Ni, Co), produced NH3 at rates similar to conventional metal nitride catalysts. Learn more about sustainable fertilizer production in our latest article .

Advancements in RNA medicine

While the application of mRNA in COVID-19 vaccines garnered lots of attention, the real revolution of RNA technology is just beginning. Recently, a new multivalent nucleoside-modified mRNA flu vaccine was developed. This vaccine has the potential to build immune protection against any of the 20 known subtypes of influenza virus and protect against future outbreaks. Many rare genetic diseases are the next target for mRNA therapies, as they are often missing a vital protein and could be cured by replacing a healthy protein through mRNA therapy. In addition to mRNA therapies, the clinical pipeline has many RNA therapeutic candidates for multiple forms of cancers, and blood and lung diseases. RNA is highly targeted, versatile, and easily customized, which makes it applicable to a wide range of diseases. Learn more about the crowded clinical pipeline and the emerging trends in RNA technologies in our latest CAS Insight Report.

Rapid skeletal transformation

Within synthetic chemistry, the challenge of safely exchanging a single atom in a molecular framework or inserting and deleting single atoms from a molecular skeleton has been formidable. While many methods have been developed to functionalize molecules with peripheral substituents (such as C-H activation), one of the first methods to perform single-atom modifications on the skeletons of organic compounds was developed by Mark Levin’s group at the University of Chicago . This enables selective cleaving of the N–N bond of pyrazole and indazole cores to afford pyrimidines and quinazolines. Further development of skeletal editing methods would enable rapid diversification of commercially available molecules, which could lead to much faster discoveries of functional molecules and ideal drug candidates.

Advancing limb regeneration

Limb loss is projected to affect over 3.6 million individuals per year by 2050. For the longest time, scientists believed the single biggest key to limb regeneration is the presence of nerves. However, work done by Dr. Muneoka and his team demonstrated the importance of mechanical load to digit regeneration in mammals and that the absence of a nerve does not inhibit regeneration. The advancement of limb regeneration was also achieved by researchers at Tufts University who have used acute multidrug delivery , via a wearable bioreactor, to successfully enable long-term limb regeneration in frogs. This early success could potentially lead to larger, more complex tissue re-engineering advances for humans, eventually benefiting military veterans, diabetics, and others impacted by amputation and trauma.

Nuclear fusion generates more net energy with ignition

Nuclear fusion is the process that powers the sun and stars. For decades, the idea of replicating nuclear fusion on earth as a source of energy, in theory, could fulfill all the planet's future energy needs. The goal is to force light atoms to collide so forcefully that they fuse and release more energy than consumed. However, overcoming the electrical repulsion between the positive nuclei requires high temperatures and pressures. Once overcome, fusion releases large amounts of energy, which should also drive the fusion of nearby nuclei. Previous attempts to initiate fusion used strong magnetic fields and powerful lasers but had been unable to generate more energy than they consumed.

Researchers at Lawrence Livermore National Laboratory’s ignition facility reported that the team was able to initiate nuclear fusion, which created 3.15 megajoules of energy from the 2.05 megajoule laser used. While this is a monumental breakthrough, the reality of a functioning nuclear fusion plant powering our grid may still be decades in the making. There are significant implementation hurdles (scalability, plant safety, energy required to generate the laser, wasted by-products, etc.) that must be addressed before this comes to fruition. However, the breakthrough of igniting nuclear fusion is a major milestone that will pave the way for future progress to be built upon this achievement.

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Global activity of seafloor biodiversity mapped

A team of scientists from the USA and UK has used artificial intelligence (AI) to map the activities of seafloor invertebrate animals, such as worms, clams and shrimps, across all the oceans of the world.

The research, led by Texas A&M University (USA) with investigators from the University of Southampton (UK) and Yale University (USA), combined large datasets, with machine learning techniques, to reveal the critical factors that support and maintain the health of marine ecosystems.

Marine sediments are extremely diverse and cover the majority of the Earth's surface. By stirring up and churning the seafloor -- a process known as 'bioturbation' -- small creatures living in the sediments can have a big impact in regulating global carbon, nutrient and biogeochemical cycles. Rather like worms turning and enriching the soil in our garden, invertebrates are doing the same on the seabed -- improving conditions for ocean life.

Understanding how these processes operate in different regions of the world gives scientists important insights into what is driving the health of oceans and how they may respond to climate change.

This latest study hugely expands this knowledge by, for the first time, providing a way to predict and map the contributions seafloor creatures make at any point around the world.

Findings of the study are published in the journal Current Biology.

"Knowing how bioturbation links to other aspects of the environment means that we are now better equipped to predict how these systems might change in response to climate change," commented Dr Shuang Zhang, lead researcher and assistant professor at the Department of Oceanography, Texas A&M University.

Dr Martin Solan, Professor of Marine Ecology at the University of Southampton adds: "We have known for some time that ocean sediments are extremely diverse and play a fundamental role in mediating the health of the ocean, but only now do we have insights about where, and by how much, these communities contribute. For example, the way in which these communities affect important aspects of ocean ecosystems are very different between the coastlines and the deep sea."

The researchers used existing datasets on sea creature activity and the depth of their sediment mixing -- data sourced from hundreds of test points around the world. By using this information to train from, and relating it to a variety of environmental conditions, the AI was able to make accurate predictions about what is happening in sediment on the seafloor, at any point globally.

The team found that a complex combination of a variety of environment conditions influence bioturbation and that this varies around the world. A multitude of factors, such as water depth, temperature, salinity, distance from land, animal abundance and nutrient availability all play a role. In turn, this affects the activity of invertebrate animals and ultimately the health of ocean ecosystems.

"Through our analysis, we discovered that not just one, but multiple environmental factors jointly influence seafloor bioturbation and the ecosystem services these animals provide," Dr Lidya Tarhan, Assistant Professor at the Department of Earth and Planetary Sciences, Yale University, said. "This includes factors that directly impact food supply, underlying the complex relationships that sustain marine life, both today and in Earth's past."

The team hope their study will help with developing strategies to mitigate habitat deterioration and protect marine biodiversity.

"Our analysis suggests that the present global network of marine protected areas does not sufficiently protect these important seafloor processes, indicating that protection measures need to be better catered to promote ecosystem health." added Dr Lidya Tarhan.

Marine Biology
Ecology Research
Oceanography
Environmental Awareness
Dog intelligence
The evolution of human intelligence
Game theory
Paleoclimatology

Story Source:

Materials provided by University of Southampton . Note: Content may be edited for style and length.

Journal Reference :

Shuang Zhang, Martin Solan, Lidya Tarhan. Global distribution and environmental correlates of marine bioturbation . Current Biology , 2024; DOI: 10.1016/j.cub.2024.04.065

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Assessing the evolution of research topics in a biological field using plant science as an example

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America, Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, Michigan, United States of America, DOE-Great Lake Bioenergy Research Center, Michigan State University, East Lansing, Michigan, United States of America

Roles Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

Affiliation Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America

Shin-Han Shiu,
Melissa D. Lehti-Shiu

Published: May 23, 2024
https://doi.org/10.1371/journal.pbio.3002612
Peer Review
Reader Comments

Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

Citation: Shiu S-H, Lehti-Shiu MD (2024) Assessing the evolution of research topics in a biological field using plant science as an example. PLoS Biol 22(5): e3002612. https://doi.org/10.1371/journal.pbio.3002612

Academic Editor: Ulrich Dirnagl, Charite Universitatsmedizin Berlin, GERMANY

Received: October 16, 2023; Accepted: April 4, 2024; Published: May 23, 2024

Copyright: © 2024 Shiu, Lehti-Shiu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The plant science corpus data are available through Zenodo ( https://zenodo.org/records/10022686 ). The codes for the entire project are available through GitHub ( https://github.com/ShiuLab/plant_sci_hist ) and Zenodo ( https://doi.org/10.5281/zenodo.10894387 ).

Funding: This work was supported by the National Science Foundation (IOS-2107215 and MCB-2210431 to MDL and SHS; DGE-1828149 and IOS-2218206 to SHS), Department of Energy grant Great Lakes Bioenergy Research Center (DE-SC0018409 to SHS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: BERT, Bidirectional Encoder Representations from Transformers; br, brassinosteroid; ccTLD, country code Top Level Domain; c-Tf-Idf, class-based Tf-Idf; ChatGPT, Chat Generative Pretrained Transformer; ga, gibberellic acid; LOWESS, locally weighted scatterplot smoothing; MeSH, Medical Subject Heading; SHAP, SHapley Additive exPlanations; SJR, SCImago Journal Rank; Tf-Idf, Term frequency-Inverse document frequency; UMAP, Uniform Manifold Approximation and Projection

Introduction

The explosive growth of scientific data in recent years has been accompanied by a rapidly increasing volume of literature. These records represent a major component of our scientific knowledge and embody the history of conceptual and technological advances in various fields over time. Our ability to wade through these records is important for identifying relevant literature for specific topics, a crucial practice of any scientific pursuit [ 1 ]. Classifying the large body of literature into topics can provide a useful means to identify relevant literature. In addition, these topics offer an opportunity to assess how scientific fields have evolved and when major shifts in took place. However, such classification is challenging because the relevant articles in any topic or domain can number in the tens or hundreds of thousands, and the literature is in the form of natural language, which takes substantial effort and expertise to process [ 2 , 3 ]. In addition, even if one could digest all literature in a field, it would still be difficult to quantify such knowledge.

In the last several years, there has been a quantum leap in natural language processing approaches due to the feasibility of building complex deep learning models with highly flexible architectures [ 4 , 5 ]. The development of large language models such as Bidirectional Encoder Representations from Transformers (BERT; [ 6 ]) and Chat Generative Pretrained Transformer (ChatGPT; [ 7 ]) has enabled the analysis, generation, and modeling of natural language texts in a wide range of applications. The success of these applications is, in large part, due to the feasibility of considering how the same words are used in different contexts when modeling natural language [ 6 ]. One such application is topic modeling, the practice of establishing statistical models of semantic structures underlying a document collection. Topic modeling has been proposed for identifying scientific hot topics over time [ 1 ], for example, in synthetic biology [ 8 ], and it has also been applied to, for example, automatically identify topical scenes in images [ 9 ] and social network topics [ 10 ], discover gene programs highly correlated with cancer prognosis [ 11 ], capture “chromatin topics” that define cell-type differences [ 12 ], and investigate relationships between genetic variants and disease risk [ 13 ]. Here, we use topic modeling to ask how research topics in a scientific field have evolved and what major changes in the research trends have taken place, using plant science as an example.

Plant science corpora allow classification of major research topics

Plant science, broadly defined, is the study of photosynthetic species, their interactions with biotic/abiotic environments, and their applications. For modeling plant science topical evolution, we first identified a collection of plant science documents (i.e., corpus) using a text classification approach. To this end, we first collected over 30 million PubMed records and narrowed down candidate plant science records by searching for those with plant-related terms and taxon names (see Materials and methods ). Because there remained a substantial number of false positives (i.e., biomedical records mentioning plants in passing), a set of positive plant science examples from the 17 plant science journals with the highest numbers of plant science publications covering a wide range of subfields and a set of negative examples from journals with few candidate plant science records were used to train 4 types of text classification models (see Materials and methods ). The best text classification model performed well (F1 = 0.96, F1 of a naïve model = 0.5, perfect model = 1) where the positive and negative examples were clearly separated from each other based on prediction probability of the hold-out testing dataset (false negative rate = 2.6%, false positive rate = 5.2%, S1A and S1B Fig ). The false prediction rate for documents from the 17 plant science journals annotated with the Medical Subject Heading (MeSH) term “Plants” in NCBI was 11.7% (see Materials and methods ). The prediction probability distribution of positive instances with the MeSH term has an expected left-skew to lower values ( S1C Fig ) compared with the distributions of all positive instances ( S1A Fig ). Thus, this subset with the MeSH term is a skewed representation of articles from these 17 major plant science journals. To further benchmark the validity of the plant science records, we also conducted manual annotation of 100 records where the false positive and false negative rates were 14.6% and 10.6%, respectively (see Materials and methods ). Using 12 other plant science journals not included as positive examples as benchmarks, the false negative rate was 9.9% (see Materials and methods ). Considering the range of false prediction rate estimates with different benchmarks, we should emphasize that the model built with the top 17 plant science journals represents a substantial fraction of plant science publications but with biases. Applying the model to the candidate plant science record led to 421,658 positive predictions, hereafter referred to as “plant science records” ( S1D Fig and S1 Data ).

To better understand how the models classified plant science articles, we identified important terms from a more easily interpretable model (Term frequency-Inverse document frequency (Tf-Idf) model; F1 = 0.934) using Shapley Additive Explanations [ 14 ]; 136 terms contributed to predicting plant science records (e.g., Arabidopsis, xylem, seedling) and 138 terms contributed to non-plant science record predictions (e.g., patients, clinical, mice; Tf-Idf feature sheet, S1 Data ). Plant science records as well as PubMed articles grew exponentially from 1950 to 2020 ( Fig 1A ), highlighting the challenges of digesting the rapidly expanding literature. We used the plant science records to perform topic modeling, which consisted of 4 steps: representing each record as a BERT embedding, reducing dimensionality, clustering, and identifying the top terms by calculating class (i.e., topic)-based Tf-Idf (c-Tf-Idf; [ 15 ]). The c-Tf-Idf represents the frequency of a term in the context of how rare the term is to reduce the influence of common words. SciBERT [ 16 ] was the best model among those tested ( S2 Data ) and was used for building the final topic model, which classified 372,430 (88.3%) records into 90 topics defined by distinct combinations of terms ( S3 Data ). The topics contained 620 to 16,183 records and were named after the top 4 to 5 terms defining the topical areas ( Fig 1B and S3 Data ). For example, the top 5 terms representing the largest topic, topic 61 (16,183 records), are “qtl,” “resistance,” “wheat,” “markers,” and “traits,” which represent crop improvement studies using quantitative genetics.

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(A) Numbers of PubMed (magenta) and plant science (green) records between 1950 and 2020. (a, b, c) Coefficients of the exponential function, y = ae b . Data for the plot are in S1 Data . (B) Numbers of documents for the top 30 plant science topics. Each topic is designated by an index number (left) and the top 4–6 terms with the highest cTf-Idf values (right). Data for the plot are in S3 Data . (C) Two-dimensional representation of the relationships between plant science records generated by Uniform Manifold Approximation and Projection (UMAP, [ 17 ]) using SciBERT embeddings of plant science records. All topics panel: Different topics are assigned different colors. Outlier panel: UMAP representation of all records (gray) with outlier records in red. Blue dotted circles: areas with relatively high densities indicating topics that are below the threshold for inclusion in a topic. In the 8 UMAP representations on the right, records for example topics are in red and the remaining records in gray. Blue dotted circles indicate the relative position of topic 48.

https://doi.org/10.1371/journal.pbio.3002612.g001

Records with assigned topics clustered into distinct areas in a two-dimensional (2D) space ( Fig 1C , for all topics, see S4 Data ). The remaining 49,228 outlier records not assigned to any topic (11.7%, middle panel, Fig 1C ) have 3 potential sources. First, some outliers likely belong to unique topics but have fewer records than the threshold (>500, blue dotted circles, Fig 1C ). Second, some of the many outliers dispersed within the 2D space ( Fig 1C ) were not assigned to any single topic because they had relatively high prediction scores for multiple topics ( S2 Fig ). These likely represent studies across subdisciplines in plant science. Third, some outliers are likely interdisciplinary studies between plant science and other domains, such as chemistry, mathematics, and physics. Such connections can only be revealed if records from other domains are included in the analyses.

Topical clusters reveal closely related topics but with distinct key term usage

Related topics tend to be located close together in the 2D representation (e.g., topics 48 and 49, Fig 1C ). We further assessed intertopical relationships by determining the cosine similarities between topics using cTf-Idfs ( Figs 2A and S3 ). In this topic network, some topics are closely related and form topic clusters. For example, topics 25, 26, and 27 collectively represent a more general topic related to the field of plant development (cluster a , lower left in Fig 2A ). Other topic clusters represent studies of stress, ion transport, and heavy metals ( b ); photosynthesis, water, and UV-B ( c ); population and community biology (d); genomics, genetic mapping, and phylogenetics ( e , upper right); and enzyme biochemistry ( f , upper left in Fig 2A ).

(A) Graph depicting the degrees of similarity (edges) between topics (nodes). Between each topic pair, a cosine similarity value was calculated using the cTf-Idf values of all terms. A threshold similarity of 0.6 was applied to illustrate the most related topics. For the full matrix presented as a heatmap, see S4 Fig . The nodes are labeled with topic index numbers and the top 4–6 terms. The colors and width of the edges are defined based on cosine similarity. Example topic clusters are highlighted in yellow and labeled a through f (blue boxes). (B, C) Relationships between the cTf-Idf values (see S3 Data ) of the top terms for topics 26 and 27 (B) and for topics 25 and 27 (C) . Only terms with cTf-Idf ≥ 0.6 are labeled. Terms with cTf-Idf values beyond the x and y axis limit are indicated by pink arrows and cTf-Idf values. (D) The 2D representation in Fig 1C is partitioned into graphs for different years, and example plots for every 5-year period since 1975 are shown. Example topics discussed in the text are indicated. Blue arrows connect the areas occupied by records of example topics across time periods to indicate changes in document frequencies.

https://doi.org/10.1371/journal.pbio.3002612.g002

Topics differed in how well they were connected to each other, reflecting how general the research interests or needs are (see Materials and methods ). For example, topic 24 (stress mechanisms) is the most well connected with median cosine similarity = 0.36, potentially because researchers in many subfields consider aspects of plant stress even though it is not the focus. The least connected topics include topic 21 (clock biology, 0.12), which is surprising because of the importance of clocks in essentially all aspects of plant biology [ 18 ]. This may be attributed, in part, to the relatively recent attention in this area.

Examining topical relationships and the cTf-Idf values of terms also revealed how related topics differ. For example, topic 26 is closely related to topics 27 and 25 (cluster a on the lower left of Fig 2A ). Topics 26 and 27 both contain records of developmental process studies mainly in Arabidopsis ( Fig 2B ); however, topic 26 is focused on the impact of light, photoreceptors, and hormones such as gibberellic acids (ga) and brassinosteroids (br), whereas topic 27 is focused on flowering and floral development. Topic 25 is also focused on plant development but differs from topic 27 because it contains records of studies mainly focusing on signaling and auxin with less emphasis on Arabidopsis ( Fig 2C ). These examples also highlight the importance of using multiple top terms to represent the topics. The similarities in cTf-Idfs between topics were also useful for measuring the editorial scope (i.e., diverse, or narrow) of journals publishing plant science papers using a relative topic diversity measure (see Materials and methods ). For example, Proceedings of the National Academy of Sciences , USA has the highest diversity, while Theoretical and Applied Genetics has the lowest ( S4 Fig ). One surprise is the relatively low diversity of American Journal of Botany , which focuses on plant ecology, systematics, development, and genetics. The low diversity is likely due to the relatively larger number of cellular and molecular science records in PubMed, consistent with the identification of relatively few topical areas relevant to studies at the organismal, population, community, and ecosystem levels.

Investigation of the relative prevalence of topics over time reveals topical succession

We next asked whether relationships between topics reflect chronological progression of certain subfields. To address this, we assessed how prevalent topics were over time using dynamic topic modeling [ 19 ]. As shown in Fig 2D , there is substantial fluctuation in where the records are in the 2D space over time. For example, topic 44 (light, leaves, co, synthesis, photosynthesis) is among the topics that existed in 1975 but has diminished gradually since. In 1985, topic 39 (Agrobacterium-based transformation) became dense enough to be visualized. Additional examples include topics 79 (soil heavy metals), 42 (differential expression), and 82 (bacterial community metagenomics), which became prominent in approximately 2005, 2010, and 2020, respectively ( Fig 2D ). In addition, animating the document occupancy in the 2D space over time revealed a broad change in patterns over time: Some initially dense areas became sparse over time and a large number of topics in areas previously only loosely occupied at the turn of the century increased over time ( S5 Data ).

While the 2D representations reveal substantial details on the evolution of topics, comparison over time is challenging because the number of plant science records has grown exponentially ( Fig 1A ). To address this, the records were divided into 50 chronological bins each with approximately 8,400 records to make cross-bin comparisons feasible ( S6 Data ). We should emphasize that, because of the way the chronological bins were split, the number of records for each topic in each bin should be treated as a normalized value relative to all other topics during the same period. Examining this relative prevalence of topics across bins revealed a clear pattern of topic succession over time (one topic evolved into another) and the presence of 5 topical categories ( Fig 3 ). The topics were categorized based on their locally weighted scatterplot smoothing (LOWESS) fits and ordered according to timing of peak frequency ( S7 and S8 Data , see Materials and methods ). In Fig 3 , the relative decrease in document frequency does not mean that research output in a topic is dwindling. Because each row in the heatmap is normalized based on the minimum and maximum values within each topic, there still can be substantial research output in terms of numbers of publications even when the relative frequency is near zero. Thus, a reduced relative frequency of a topic reflects only a below-average growth rate compared with other topical areas.

(A-E) A heat map of relative topic frequency over time reveals 5 topical categories: (A) stable, (B) early, (C) transitional, (D) sigmoidal, and (E) rising. The x axis denotes different time bins with each bin containing a similar number of documents to account for the exponential growth of plant science records over time. The sizes of all bins except the first are drawn to scale based on the beginning and end dates. The y axis lists different topics denoted by the label and top 4 to 5 terms. In each cell, the prevalence of a topic in a time bin is colored according to the min-max normalized cTf-Idf values for that topic. Light blue dotted lines delineate different decades. The arrows left of a subset of topic labels indicate example relationships between topics in topic clusters. Blue boxes with labels a–f indicate topic clusters, which are the same as those in Fig 2 . Connecting lines indicate successional trends. Yellow circles/lines 1 – 3: 3 major transition patterns. The original data are in S5 Data .

https://doi.org/10.1371/journal.pbio.3002612.g003

The first topical category is a stable category with 7 topics mostly established before the 1980s that have since remained stable in terms of prevalence in the plant science records (top of Fig 3A ). These topics represent long-standing plant science research foci, including studies of plant physiology (topics 4, 58, and 81), genetics (topic 61), and medicinal plants (topic 53). The second category contains 8 topics established before the 1980s that have mostly decreased in prevalence since (the early category, Fig 3B ). Two examples are physiological and morphological studies of hormone action (topic 45, the second in the early category) and the characterization of protein, DNA, and RNA (topic 18, the second to last). Unlike other early topics, topic 78 (paleobotany and plant evolution studies, the last topic in Fig 3B ) experienced a resurgence in the early 2000s due to the development of new approaches and databases and changes in research foci [ 20 ].

The 33 topics in the third, transitional category became prominent in the 1980s, 1990s, or even 2000s but have clearly decreased in prevalence ( Fig 3C ). In some cases, the early and the transitional topics became less prevalent because of topical succession—refocusing of earlier topics led to newer ones that either show no clear sign of decrease (the sigmoidal category, Fig 3D ) or continue to increase in prevalence (the rising category, Fig 3E ). Consistent with the notion of topical succession, topics within each topic cluster ( Fig 2 ) were found across topic categories and/or were prominent at different time periods (indicated by colored lines linking topics, Fig 3 ). One example is topics in topic cluster b (connected with light green lines and arrows, compare Figs 2 and 3 ); the study of cation transport (topic 47, the third in the transitional category), prominent in the 1980s and early 1990s, is connected to 5 other topics, namely, another transitional topic 29 (cation channels and their expression) peaking in the 2000s and early 2010s, sigmoidal topics 24 and 28 (stress response, tolerance mechanisms) and 30 (heavy metal transport), which rose to prominence in mid-2000s, and the rising topic 42 (stress transcriptomic studies), which increased in prevalence in the mid-2010s.

The rise and fall of topics can be due to a combination of technological or conceptual breakthroughs, maturity of the field, funding constraints, or publicity. The study of transposable elements (topic 62) illustrates the effect of publicity; the rise in this field coincided with Barbara McClintock’s 1983 Nobel Prize but not with the publication of her studies in the 1950s [ 21 ]. The reduced prevalence in early 2000 likely occurred in part because analysis of transposons became a central component of genome sequencing and annotation studies, rather than dedicated studies. In addition, this example indicates that our approaches, while capable of capturing topical trends, cannot be used to directly infer major papers leading to the growth of a topic.

Three major topical transition patterns signify shifts in research trends

Beyond the succession of specific topics, 3 major transitions in the dynamic topic graph should be emphasized: (1) the relative decreasing trend of early topics in the late 1970s and early 1980s; (2) the rise of transitional topics in late 1980s; and (3) the relative decreasing trend of transitional topics in the late 1990s and early 2000s, which coincided with a radiation of sigmoidal and rising topics (yellow circles, Fig 3 ). The large numbers of topics involved in these transitions suggest major shifts in plant science research. In transition 1, early topics decreased in relative prevalence in the late 1970s to early 1980s, which coincided with the rise of transitional topics over the following decades (circle 1, Fig 3 ). For example, there was a shift from the study of purified proteins such as enzymes (early topic 48, S5A Fig ) to molecular genetic dissection of genes, proteins, and RNA (transitional topic 35, S5B Fig ) enabled by the wider adoption of recombinant DNA and molecular cloning technologies in late 1970s [ 22 ]. Transition 2 (circle 2, Fig 3 ) can be explained by the following breakthroughs in the late 1980s: better approaches to create transgenic plants and insertional mutants [ 23 ], more efficient creation of mutant plant libraries through chemical mutagenesis (e.g., [ 24 ]), and availability of gene reporter systems such as β-glucuronidase [ 25 ]. Because of these breakthroughs, molecular genetics studies shifted away from understanding the basic machinery to understanding the molecular underpinnings of specific processes, such as molecular mechanisms of flower and meristem development and the action of hormones such as auxin (topic 27, S5C Fig ); this type of research was discussed as a future trend in 1988 [ 26 ] and remains prevalent to this date. Another example is gene silencing (topic 12), which became a focal area of study along with the widespread use of transgenic plants [ 27 ].

Transition 3 is the most drastic: A large number of transitional, sigmoidal, and rising topics became prevalent nearly simultaneously at the turn of the century (circle 3, Fig 3 ). This period also coincides with a rapid increase in plant science citations ( Fig 1A ). The most notable breakthroughs included the availability of the first plant genome in 2000 [ 28 ], increasing ease and reduced cost of high-throughput sequencing [ 29 ], development of new mass spectrometry–based platforms for analyzing proteins [ 30 ], and advancements in microscopic and optical imaging approaches [ 31 ]. Advances in genomics and omics technology also led to an increase in stress transcriptomics studies (42, S5D Fig ) as well as studies in many other topics such as epigenetics (topic 11), noncoding RNA analysis (13), genomics and phylogenetics (80), breeding (41), genome sequencing and assembly (60), gene family analysis (23), and metagenomics (82 and 55).

In addition to the 3 major transitions across all topics, there were also transitions within topics revealed by examining the top terms for different time bins (heatmaps, S5 Fig ). Taken together, these observations demonstrate that knowledge about topical evolution can be readily revealed through topic modeling. Such knowledge is typically only available to experts in specific areas and is difficult to summarize manually, as no researcher has a command of the entire plant science literature.

Analysis of taxa studied reveals changes in research trends

Changes in research trends can also be illustrated by examining changes in the taxa being studied over time ( S9 Data ). There is a strong bias in the taxa studied, with the record dominated by research models and economically important taxa ( S6 Fig ). Flowering plants (Magnoliopsida) are found in 93% of records ( S6A Fig ), and the mustard family Brassicaceae dominates at the family level ( S6B Fig ) because the genus Arabidopsis contributes to 13% of plant science records ( Fig 4A ). When examining the prevalence of taxa being studied over time, clear patterns of turnover emerged similar to topical succession ( Figs 4B , S6C, and S6D ; Materials and methods ). Given that Arabidopsis is mentioned in more publications than other species we analyzed, we further examined the trends for Arabidopsis publications. The increase in the normalized number (i.e., relative to the entire plant science corpus) of Arabidopsis records coincided with advocacy of its use as a model system in the late 1980s [ 32 ]. While it remains a major plant model, there has been a decrease in overall Arabidopsis publications relative to all other plant science publications since 2011 (blue line, normalized total, Fig 4C ). Because the same chronological bins, each with same numbers of records, from the topic-over-time analysis ( Fig 3 ) were used, the decrease here does not mean that there were fewer Arabidopsis publications—in fact, the number of Arabidopsis papers has remained steady since 2011. This decrease means that Arabidopsis-related publications represent a relatively smaller proportion of plant science records. Interestingly, this decrease took place much earlier (approximately 2005) and was steeper in the United States (red line, Fig 4C ) than in all countries combined (blue line, Fig 4C ).

(A) Percentage of records mentioning specific genera. (B) Change in the prevalence of genera in plant science records over time. (C) Changes in the normalized numbers of all records (blue) and records from the US (red) mentioning Arabidopsis over time. The lines are LOWESS fits with fraction parameter = 0.2. (D) Topical over (red) and under (blue) representation among 5 genera with the most plant science records. LLR: log 2 likelihood ratios of each topic in each genus. Gray: topic-species combination not significantly enriched at the 5% level based on enrichment p -values adjusted for multiple testing with the Benjamini–Hochberg method [ 33 ]. The data used for plotting are in S9 Data . The statistics for all topics are in S10 Data .

https://doi.org/10.1371/journal.pbio.3002612.g004

Assuming that the normalized number of publications reflects the relative intensity of research activities, one hypothesis for the relative decrease in focus on Arabidopsis is that advances in, for example, plant transformation, genetic manipulation, and genome research have allowed the adoption of more previously nonmodel taxa. Consistent with this, there was a precipitous increase in the number of genera being published in the mid-90s to early 2000s during which approaches for plant transgenics became established [ 34 ], but the number has remained steady since then ( S7A Fig ). The decrease in the proportion of Arabidopsis papers is also negatively correlated with the timing of an increase in the number of draft genomes ( S7B Fig and S9 Data ). It is plausible that genome availability for other species may have contributed to a shift away from Arabidopsis. Strikingly, when we analyzed US National Science Foundation records, we found that the numbers of funded grants mentioning Arabidopsis ( S7C Fig ) have risen and fallen in near perfect synchrony with the normalized number of Arabidopsis publication records (red line, Fig 4C ). This finding likely illustrates the impact of funding on Arabidopsis research.

By considering both taxa information and research topics, we can identify clear differences in the topical areas preferred by researchers using different plant taxa ( Fig 4D and S10 Data ). For example, studies of auxin/light signaling, the circadian clock, and flowering tend to be carried out in Arabidopsis, while quantitative genetic studies of disease resistance tend to be done in wheat and rice, glyphosate research in soybean, and RNA virus research in tobacco. Taken together, joint analyses of topics and species revealed additional details about changes in preferred models over time, and the preferred topical areas for different taxa.

Countries differ in their contributions to plant science and topical preference

We next investigated whether there were geographical differences in topical preference among countries by inferring country information from 330,187 records (see Materials and methods ). The 10 countries with the most records account for 73% of the total, with China and the US contributing to approximately 18% each ( Fig 5A ). The exponential growth in plant science records (green line, Fig 1A ) was in large part due to the rapid rise in annual record numbers in China and India ( Fig 5B ). When we examined the publication growth rates using the top 17 plant science journals, the general patterns remained the same ( S7D Fig ). On the other hand, the US, Japan, Germany, France, and Great Britain had slower rates of growth compared with all non-top 10 countries. The rapid increase in records from China and India was accompanied by a rapid increase in metrics measuring journal impact ( Figs 5C and S8 and S9 Data ). For example, using citation score ( Fig 5C , see Materials and methods ), we found that during a 22-year period China (dark green) and India (light green) rapidly approached the global average (y = 0, yellow), whereas some of the other top 10 countries, particularly the US (red) and Japan (yellow green), showed signs of decrease ( Fig 5C ). It remains to be determined whether these geographical trends reflect changes in priority, investment, and/or interest in plant science research.

(A) Numbers of plant science records for countries with the 10 highest numbers. (B) Percentage of all records from each of the top 10 countries from 1980 to 2020. (C) Difference in citation scores from 1999 to 2020 for the top 10 countries. (D) Shown for each country is the relationship between the citation scores averaged from 1999 to 2020 and the slope of linear fit with year as the predictive variable and citation score as the response variable. The countries with >400 records and with <10% missing impact values are included. Data used for plots (A–D) are in S11 Data . (E) Correlation in topic enrichment scores between the top 10 countries. PCC, Pearson’s correlation coefficient, positive in red, negative in blue. Yellow rectangle: countries with more similar topical preferences. (F) Enrichment scores (LLR, log likelihood ratio) of selected topics among the top 10 countries. Red: overrepresentation, blue: underrepresentation. Gray: topic-country combination that is not significantly enriched at the 5% level based on enrichment p -values adjusted for multiple testing with the Benjamini–Hochberg method (for all topics and plotting data, see S12 Data ).

https://doi.org/10.1371/journal.pbio.3002612.g005

Interestingly, the relative growth/decline in citation scores over time (measured as the slope of linear fit of year versus citation score) was significantly and negatively correlated with average citation score ( Fig 5D ); i.e., countries with lower overall metrics tended to experience the strongest increase in citation scores over time. Thus, countries that did not originally have a strong influence on plant sciences now have increased impact. These patterns were also observed when using H-index or journal rank as metrics ( S8 Fig and S11 Data ) and were not due to increased publication volume, as the metrics were normalized against numbers of records from each country (see Materials and methods ). In addition, the fact that different metrics with different caveats and assumptions yielded consistent conclusions indicates the robustness of our observations. We hypothesize that this may be a consequence of the ease in scientific communication among geographically isolated research groups. It could also be because of the prevalence of online journals that are open access, which makes scientific information more readily accessible. Or it can be due to the increasing international collaboration. In any case, the causes for such regression toward the mean are not immediately clear and should be addressed in future studies.

We also assessed how the plant research foci of countries differ by comparing topical preference (i.e., the degree of enrichment of plant science records in different topics) between countries. For example, Italy and Spain cluster together (yellow rectangle, Fig 5E ) partly because of similar research focusing on allergens (topic 0) and mycotoxins (topic 54) and less emphasis on gene family (topic 23) and stress tolerance (topic 28) studies ( Fig 5F , for the fold enrichment and corrected p -values of all topics, see S12 Data ). There are substantial differences in topical focus between countries ( S9 Fig ). For example, research on new plant compounds associated with herbal medicine (topic 69) is a focus in China but not in the US, but the opposite is true for population genetics and evolution (topic 86) ( Fig 5F ). In addition to revealing how plant science research has evolved over time, topic modeling provides additional insights into differences in research foci among different countries, which are informative for science policy considerations.

In this study, topic modeling revealed clear transitions among research topics, which represent shifts in research trends in plant sciences. One limitation of our study is the bias in the PubMed-based corpus. The cellular, molecular, and physiological aspects of plant sciences are well represented, but there are many fewer records related to evolution, ecology, and systematics. Our use of titles/abstracts from the top 17 plant science journals as positive examples allowed us to identify papers we typically see in these journals, but this may have led to us missing “outlier” articles, which may be the most exciting. Another limitation is the need to assign only one topic to a record when a study is interdisciplinary and straddles multiple topics. Furthermore, a limited number of large, inherently heterogeneous topics were summarized to provide a more concise interpretation, which undoubtedly underrepresents the diversity of plant science research. Despite these limitations, dynamic topic modeling revealed changes in plant science research trends that coincide with major shifts in biological science. While we were interested in identifying conceptual advances, our approach can identify the trend but the underlying causes for such trends, particularly key records leading to the growth in certain topics, still need to be identified. It also remains to be determined which changes in research trends lead to paradigm shifts as defined by Kuhn [ 35 ].

The key terms defining the topics frequently describe various technologies (e.g., topic 38/39: transformation, 40: genome editing, 59: genetic markers, 65: mass spectrometry, 69: nuclear magnetic resonance) or are indicative of studies enabled through molecular genetics and omics technologies (e.g., topic 8/60: genome, 11: epigenetic modifications, 18: molecular biological studies of macromolecules, 13: small RNAs, 61: quantitative genetics, 82/84: metagenomics). Thus, this analysis highlights how technological innovation, particularly in the realm of omics, has contributed to a substantial number of research topics in the plant sciences, a finding that likely holds for other scientific disciplines. We also found that the pattern of topic evolution is similar to that of succession, where older topics have mostly decreased in relative prevalence but appear to have been superseded by newer ones. One example is the rise of transcriptome-related topics and the correlated, reduced focus on regulation at levels other than transcription. This raises the question of whether research driven by technology negatively impacts other areas of research where high-throughput studies remain challenging.

One observation on the overall trends in plant science research is the approximately 10-year cycle in major shifts. One hypothesis is related to not only scientific advances but also to the fashion-driven aspect of science. Nonetheless, given that there were only 3 major shifts and the sample size is small, it is difficult to speculate as to why they happened. By analyzing the country of origin, we found that China and India have been the 2 major contributors to the growth in the plant science records in the last 20 years. Our findings also show an equalizing trend in global plant science where countries without a strong plant science publication presence have had an increased impact over the last 20 years. In addition, we identified significant differences in research topics between countries reflecting potential differences in investment and priorities. Such information is important for discerning differences in research trends across countries and can be considered when making policy decisions about research directions.

Materials and methods

Collection and preprocessing of a candidate plant science corpus.

For reproducibility purposes, a random state value of 20220609 was used throughout the study. The PubMed baseline files containing citation information ( ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline/ ) were downloaded on November 11, 2021. To narrow down the records to plant science-related citations, a candidate citation was identified as having, within the titles and/or abstracts, at least one of the following words: “plant,” “plants,” “botany,” “botanical,” “planta,” and “plantarum” (and their corresponding upper case and plural forms), or plant taxon identifiers from NCBI Taxonomy ( https://www.ncbi.nlm.nih.gov/taxonomy ) or USDA PLANTS Database ( https://plants.sc.egov.usda.gov/home ). Note the search terms used here have nothing to do with the values of the keyword field in PubMed records. The taxon identifiers include all taxon names including and at taxonomic levels below “Viridiplantae” till the genus level (species names not used). This led to 51,395 search terms. After looking for the search terms, qualified entries were removed if they were duplicated, lacked titles and/or abstracts, or were corrections, errata, or withdrawn articles. This left 1,385,417 citations, which were considered the candidate plant science corpus (i.e., a collection of texts). For further analysis, the title and abstract for each citation were combined into a single entry. Text was preprocessed by lowercasing, removing stop-words (i.e., common words), removing non-alphanumeric and non-white space characters (except Greek letters, dashes, and commas), and applying lemmatization (i.e., grouping inflected forms of a word as a single word) for comparison. Because lemmatization led to truncated scientific terms, it was not included in the final preprocessing pipeline.

Definition of positive/negative examples

Upon closer examination, a large number of false positives were identified in the candidate plant science records. To further narrow down citations with a plant science focus, text classification was used to distinguish plant science and non-plant science articles (see next section). For the classification task, a negative set (i.e., non-plant science citations) was defined as entries from 7,360 journals that appeared <20 times in the filtered data (total = 43,329, journal candidate count, S1 Data ). For the positive examples (i.e., true plant science citations), 43,329 plant science citations (positive examples) were sampled from 17 established plant science journals each with >2,000 entries in the filtered dataset: “Plant physiology,” “Frontiers in plant science,” “Planta,” “The Plant journal: for cell and molecular biology,” “Journal of experimental botany,” “Plant molecular biology,” “The New phytologist,” “The Plant cell,” “Phytochemistry,” “Plant & cell physiology,” “American journal of botany,” “Annals of botany,” “BMC plant biology,” “Tree physiology,” “Molecular plant-microbe interactions: MPMI,” “Plant biology,” and “Plant biotechnology journal” (journal candidate count, S1 Data ). Plant biotechnology journal was included, but only 1,894 records remained after removal of duplicates, articles with missing info, and/or withdrawn articles. The positive and negative sets were randomly split into training and testing subsets (4:1) while maintaining a 1:1 positive-to-negative ratio.

Text classification based on Tf and Tf-Idf

Instead of using the preprocessed text as features for building classification models directly, text embeddings (i.e., representations of texts in vectors) were used as features. These embeddings were generated using 4 approaches (model summary, S1 Data ): Term-frequency (Tf), Tf-Idf [ 36 ], Word2Vec [ 37 ], and BERT [ 6 ]. The Tf- and Tf-Idf-based features were generated with CountVectorizer and TfidfVectorizer, respectively, from Scikit-Learn [ 38 ]. Different maximum features (1e4 to 1e5) and n-gram ranges (uni-, bi-, and tri-grams) were tested. The features were selected based on the p- value of chi-squared tests testing whether a feature had a higher-than-expected value among the positive or negative classes. Four different p- value thresholds were tested for feature selection. The selected features were then used to retrain vectorizers with the preprocessed training texts to generate feature values for classification. The classification model used was XGBoost [ 39 ] with 5 combinations of the following hyperparameters tested during 5-fold stratified cross-validation: min_child_weight = (1, 5, 10), gamma = (0.5, 1, 1.5, 2.5), subsample = (0.6, 0.8, 1.0), colsample_bytree = (0.6, 0.8, 1.0), and max_depth = (3, 4, 5). The rest of the hyperparameters were held constant: learning_rate = 0.2, n_estimators = 600, objective = binary:logistic. RandomizedSearchCV from Scikit-Learn was used for hyperparameter tuning and cross-validation with scoring = F1-score.

Because the Tf-Idf model had a relatively high model performance and was relatively easy to interpret (terms are frequency-based, instead of embedding-based like those generated by Word2Vec and BERT), the Tf-Idf model was selected as input to SHapley Additive exPlanations (SHAP; [ 14 ]) to assess the importance of terms. Because the Tf-Idf model was based on XGBoost, a tree-based algorithm, the TreeExplainer module in SHAP was used to determine a SHAP value for each entry in the training dataset for each Tf-Idf feature. The SHAP value indicates the degree to which a feature positively or negatively affects the underlying prediction. The importance of a Tf-Idf feature was calculated as the average SHAP value of that feature among all instances. Because a Tf-Idf feature is generated based on a specific term, the importance of the Tf-Idf feature indicates the importance of the associated term.

Text classification based on Word2Vec

The preprocessed texts were first split into train, validation, and test subsets (8:1:1). The texts in each subset were converted to 3 n-gram lists: a unigram list obtained by splitting tokens based on the space character, or bi- and tri-gram lists built with Gensim [ 40 ]. Each n-gram list of the training subset was next used to fit a Skip-gram Word2Vec model with vector_size = 300, window = 8, min_count = (5, 10, or 20), sg = 1, and epochs = 30. The Word2Vec model was used to generate word embeddings for train, validate, and test subsets. In the meantime, a tokenizer was trained with train subset unigrams using Tensorflow [ 41 ] and used to tokenize texts in each subset and turn each token into indices to use as features for training text classification models. To ensure all citations had the same number of features (500), longer texts were truncated, and shorter ones were zero-padded. A deep learning model was used to train a text classifier with an input layer the same size as the feature number, an attention layer incorporating embedding information for each feature, 2 bidirectional Long-Short-Term-Memory layers (15 units each), a dense layer (64 units), and a final, output layer with 2 units. During training, adam, accuracy, and sparse_categorical_crossentropy were used as the optimizer, evaluation metric, and loss function, respectively. The training process lasted 30 epochs with early stopping if validation loss did not improve in 5 epochs. An F1 score was calculated for each n-gram list and min_count parameter combination to select the best model (model summary, S1 Data ).

Text classification based on BERT models

Two pretrained models were used for BERT-based classification: DistilBERT (Hugging face repository [ 42 ] model name and version: distilbert-base-uncased [ 43 ]) and SciBERT (allenai/scibert-scivocab-uncased [ 16 ]). In both cases, tokenizers were retrained with the training data. BERT-based models had the following architecture: the token indices (512 values for each token) and associated masked values as input layers, pretrained BERT layer (512 × 768) excluding outputs, a 1D pooling layer (768 units), a dense layer (64 units), and an output layer (2 units). The rest of the training parameters were the same as those for Word2Vec-based models, except training lasted for 20 epochs. Cross-validation F1-scores for all models were compared and used to select the best model for each feature extraction method, hyperparameter combination, and modeling algorithm or architecture (model summary, S1 Data ). The best model was the Word2Vec-based model (min_count = 20, window = 8, ngram = 3), which was applied to the candidate plant science corpus to identify a set of plant science citations for further analysis. The candidate plant science records predicted as being in the positive class (421,658) by the model were collectively referred to as the “plant science corpus.”

Plant science record classification

In PubMed, 1,384,718 citations containing “plant” or any plant taxon names (from the phylum to genus level) were considered candidate plant science citations. To further distinguish plant science citations from those in other fields, text classification models were trained using titles and abstracts of positive examples consisting of citations from 17 plant science journals, each with >2,000 entries in PubMed, and negative examples consisting of records from journals with fewer than 20 entries in the candidate set. Among 4 models tested the best model (built with Word2Vec embeddings) had a cross validation F1 of 0.964 (random guess F1 = 0.5, perfect model F1 = 1, S1 Data ). When testing the model using 17,330 testing set citations independent from the training set, the F1 remained high at 0.961.

We also conducted another analysis attempting to use the MeSH term “Plants” as a benchmark. Records with the MeSH term “Plants” also include pharmaceutical studies of plants and plant metabolites or immunological studies of plants as allergens in journals that are not generally considered plant science journals (e.g., Acta astronautica , International journal for parasitology , Journal of chromatography ) or journals from local scientific societies (e.g., Acta pharmaceutica Hungarica , Huan jing ke xue , Izvestiia Akademii nauk . Seriia biologicheskaia ). Because we explicitly labeled papers from such journals as negative examples, we focused on 4,004 records with the “Plants” MeSH term published in the 17 plant science journals that were used as positive instances and found that 88.3% were predicted as the positive class. Thus, based on the MeSH term, there is an 11.7% false prediction rate.

We also enlisted 5 plant science colleagues (3 advanced graduate students in plant biology and genetic/genome science graduate programs, 1 postdoctoral breeder/quantitative biologist, and 1 postdoctoral biochemist/geneticist) to annotate 100 randomly selected abstracts as a reviewer suggested. Each record was annotated by 2 colleagues. Among 85 entries where the annotations are consistent between annotators, 48 were annotated as negative but with 7 predicted as positive (false positive rate = 14.6%) and 37 were annotated as positive but with 4 predicted as negative (false negative rate = 10.8%). To further benchmark the performance of the text classification model, we identified another 12 journals that focus on plant science studies to use as benchmarks: Current opinion in plant biology (number of articles: 1,806), Trends in plant science (1,723), Functional plant biology (1,717), Molecular plant pathology (1,573), Molecular plant (1,141), Journal of integrative plant biology (1,092), Journal of plant research (1,032), Physiology and molecular biology of plants (830), Nature plants (538), The plant pathology journal (443). Annual review of plant biology (417), and The plant genome (321). Among the 12,611 candidate plant science records, 11,386 were predicted as positive. Thus, there is a 9.9% false negative rate.

Global topic modeling

BERTopic [ 15 ] was used for preliminary topic modeling with n-grams = (1,2) and with an embedding initially generated by DistilBERT, SciBERT, or BioBERT (dmis-lab/biobert-base-cased-v1.2; [ 44 ]). The embedding models converted preprocessed texts to embeddings. The topics generated based on the 3 embeddings were similar ( S2 Data ). However, SciBERT-, BioBERT-, and distilBERT-based embedding models had different numbers of outlier records (268,848, 293,790, and 323,876, respectively) with topic index = −1. In addition to generating the fewest outliers, the SciBERT-based model led to the highest number of topics. Therefore, SciBERT was chosen as the embedding model for the final round of topic modeling. Modeling consisted of 3 steps. First, document embeddings were generated with SentenceTransformer [ 45 ]. Second, a clustering model to aggregate documents into clusters using hdbscan [ 46 ] was initialized with min_cluster_size = 500, metric = euclidean, cluster_selection_method = eom, min_samples = 5. Third, the embedding and the initialized hdbscan model were used in BERTopic to model topics with neighbors = 10, nr_topics = 500, ngram_range = (1,2). Using these parameters, 90 topics were identified. The initial topic assignments were conservative, and 241,567 records were considered outliers (i.e., documents not assigned to any of the 90 topics). After assessing the prediction scores of all records generated from the fitted topic models, the 95-percentile score was 0.0155. This score was used as the threshold for assigning outliers to topics: If the maximum prediction score was above the threshold and this maximum score was for topic t , then the outlier was assigned to t . After the reassignment, 49,228 records remained outliers. To assess if some of the outliers were not assigned because they could be assigned to multiple topics, the prediction scores of the records were used to put records into 100 clusters using k- means. Each cluster was then assessed to determine if the outlier records in a cluster tended to have higher prediction scores across multiple topics ( S2 Fig ).

Topics that are most and least well connected to other topics

The most well-connected topics in the network include topic 24 (stress mechanisms, median cosine similarity = 0.36), topic 42 (genes, stress, and transcriptomes, 0.34), and topic 35 (molecular genetics, 0.32, all t test p -values < 1 × 10 −22 ). The least connected topics include topic 0 (allergen research, median cosine similarity = 0.12), topic 21 (clock biology, 0.12), topic 1 (tissue culture, 0.15), and topic 69 (identification of compounds with spectroscopic methods, 0.15; all t test p- values < 1 × 10 −24 ). Topics 0, 1, and 69 are specialized topics; it is surprising that topic 21 is not as well connected as explained in the main text.

Analysis of documents based on the topic model

Topical diversity among top journals with the most plant science records

Using a relative topic diversity measure (ranging from 0 to 10), we found that there was a wide range of topical diversity among 20 journals with the largest numbers of plant science records ( S3 Fig ). The 4 journals with the highest relative topical diversities are Proceedings of the National Academy of Sciences , USA (9.6), Scientific Reports (7.1), Plant Physiology (6.7), and PLOS ONE (6.4). The high diversities are consistent with the broad, editorial scopes of these journals. The 4 journals with the lowest diversities are American Journal of Botany (1.6), Oecologia (0.7), Plant Disease (0.7), and Theoretical and Applied Genetics (0.3), which reflects their discipline-specific focus and audience of classical botanists, ecologists, plant pathologists, and specific groups of geneticists.

Dynamic topic modeling

The codes for dynamic modeling were based on _topic_over_time.py in BERTopics and modified to allow additional outputs for debugging and graphing purposes. The plant science citations were binned into 50 subsets chronologically (for timestamps of bins, see S5 Data ). Because the numbers of documents increased exponentially over time, instead of dividing them based on equal-sized time intervals, which would result in fewer records at earlier time points and introduce bias, we divided them into time bins of similar size (approximately 8,400 documents). Thus, the earlier time subsets had larger time spans compared with later time subsets. If equal-size time intervals were used, the numbers of documents between the intervals would differ greatly; the earlier time points would have many fewer records, which may introduce bias. Prior to binning the subsets, the publication dates were converted to UNIX time (timestamp) in seconds; the plant science records start in 1917-11-1 (timestamp = −1646247600.0) and end in 2021-1-1 (timestamp = 1609477201). The starting dates and corresponding timestamps for the 50 subsets including the end date are in S6 Data . The input data included the preprocessed texts, topic assignments of records from global topic modeling, and the binned timestamps of records. Three additional parameters were set for topics_over_time, namely, nr_bin = 50 (number of bins), evolution_tuning = True, and global_tuning = False. The evolution_tuning parameter specified that averaged c-Tf-Idf values for a topic be calculated in neighboring time bins to reduce fluctuation in c-Tf-Idf values. The global_tuning parameter was set to False because of the possibility that some nonexisting terms could have a high c-Tf-Idf for a time bin simply because there was a high global c-Tf-Idf value for that term.

The binning strategy based on similar document numbers per bin allowed us to increase signal particularly for publications prior to the 90s. This strategy, however, may introduce more noise for bins with smaller time durations (i.e., more recent bins) because of publication frequencies (there can be seasonal differences in the number of papers published, biased toward, e.g., the beginning of the year or the beginning of a quarter). To address this, we examined the relative frequencies of each topic over time ( S7 Data ), but we found that recent time bins had similar variances in relative frequencies as other time bins. We also moderated the impact of variation using LOWESS (10% to 30% of the data points were used for fitting the trend lines) to determine topical trends for Fig 3 . Thus, the influence of the noise introduced via our binning strategy is expected to be minimal.

Topic categories and ordering

The topics were classified into 5 categories with contrasting trends: stable, early, transitional, sigmoidal, and rising. To define which category a topic belongs to, the frequency of documents over time bins for each topic was analyzed using 3 regression methods. We first tried 2 forecasting methods: recursive autoregressor (the ForecasterAutoreg class in the skforecast package) and autoregressive integrated moving average (ARIMA implemented in the pmdarima package). In both cases, the forecasting results did not clearly follow the expected trend lines, likely due to the low numbers of data points (relative frequency values), which resulted in the need to extensively impute missing data. Thus, as a third approach, we sought to fit the trendlines with the data points using LOWESS (implemented in the statsmodels package) and applied additional criteria for assigning topics to categories. When fitting with LOWESS, 3 fraction parameters (frac, the fraction of the data used when estimating each y-value) were evaluated (0.1, 0.2, 0.3). While frac = 0.3 had the smallest errors for most topics, in situations where there were outliers, frac = 0.2 or 0.1 was chosen to minimize mean squared errors ( S7 Data ).

The topics were classified into 5 categories based on the slopes of the fitted line over time: (1) stable: topics with near 0 slopes over time; (2) early: topics with negative (<−0.5) slopes throughout (with the exception of topic 78, which declined early on but bounced back by the late 1990s); (3) transitional: early positive (>0.5) slopes followed by negative slopes at later time points; (4) sigmoidal: early positive slopes followed by zero slopes at later time points; and (5) rising: continuously positive slopes. For each topic, the LOWESS fits were also used to determine when the relative document frequency reached its peak, first reaching a threshold of 0.6 (chosen after trial and error for a range of 0.3 to 0.9), and the overall trend. The topics were then ordered based on (1) whether they belonged to the stable category or not; (2) whether the trends were decreasing, stable, or increasing; (3) the time the relative document frequency first reached 0.6; and (4) the time that the overall peak was reached ( S8 Data ).

Taxa information

To identify a taxon or taxa in all plant science records, NCBI Taxonomy taxdump datasets were downloaded from the NCBI FTP site ( https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/new_taxdump/ ) on September 20, 2022. The highest-level taxon was Viridiplantae, and all its child taxa were parsed and used as queries in searches against the plant science corpus. In addition, a species-over-time analysis was conducted using the same time bins as used for dynamic topic models. The number of records in different time bins for top taxa are in the genus, family, order, and additional species level sheet in S9 Data . The degree of over-/underrepresentation of a taxon X in a research topic T was assessed using the p -value of a Fisher’s exact test for a 2 × 2 table consisting of the numbers of records in both X and T, in X but not T, in T but not X, and in neither ( S10 Data ).

For analysis of plant taxa with genome information, genome data of taxa in Viridiplantae were obtained from the NCBI Genome data-hub ( https://www.ncbi.nlm.nih.gov/data-hub/genome ) on October 28, 2022. There were 2,384 plant genome assemblies belonging to 1,231 species in 559 genera (genome assembly sheet, S9 Data ). The date of the assembly was used as a proxy for the time when a genome was sequenced. However, some species have updated assemblies and have more recent data than when the genome first became available.

Taxa being studied in the plant science records

Flowering plants (Magnoliopsida) are found in 93% of records, while most other lineages are discussed in <1% of records, with conifers and related species being exceptions (Acrogynomsopermae, 3.5%, S6A Fig ). At the family level, the mustard (Brassicaceae), grass (Poaceae), pea (Fabaceae), and nightshade (Solanaceae) families are in 51% of records ( S6B Fig ). The prominence of the mustard family in plant science research is due to the Brassica and Arabidopsis genera ( Fig 4A ). When examining the prevalence of taxa being studied over time, clear patterns of turnovers emerged ( Figs 4B , S6C, and S6D ). While the study of monocot species (Liliopsida) has remained steady, there was a significant uptick in the prevalence of eudicot (eudicotyledon) records in the late 90s ( S6C Fig ), which can be attributed to the increased number of studies in the mustard, myrtle (Myrtaceae), and mint (Lamiaceae) families among others ( S6D Fig ). At the genus level, records mentioning Gossypium (cotton), Phaseolus (bean), Hordeum (wheat), and Zea (corn), similar to the topics in the early category, were prevalent till the 1980s or 1990s but have mostly decreased in number since ( Fig 4B ). In contrast, Capsicum , Arabidopsis , Oryza , Vitus , and Solanum research has become more prevalent over the last 20 years.

Geographical information for the plant science corpus

The geographical information (country) of authors in the plant science corpus was obtained from the address (AD) fields of first authors in Medline XML records accessible through the NCBI EUtility API ( https://www.ncbi.nlm.nih.gov/books/NBK25501/ ). Because only first author affiliations are available for records published before December 2014, only the first author’s location was considered to ensure consistency between records before and after that date. Among the 421,658 records in the plant science corpus, 421,585 had Medline records and 421,276 had unique PMIDs. Among the records with unique PMIDs, 401,807 contained address fields. For each of the remaining records, the AD field content was split into tokens with a “,” delimiter, and the token likely containing geographical info (referred to as location tokens) was selected as either the last token or the second to last token if the last token contained “@” indicating the presence of an email address. Because of the inconsistency in how geographical information was described in the location tokens (e.g., country, state, city, zip code, name of institution, and different combinations of the above), the following 4 approaches were used to convert location tokens into countries.

The first approach was a brute force search where full names and alpha-3 codes of current countries (ISO 3166–1), current country subregions (ISO 3166–2), and historical country (i.e., country that no longer exists, ISO 3166–3) were used to search the address fields. To reduce false positives using alpha-3 codes, a space prior to each code was required for the match. The first approach allowed the identification of 361,242, 16,573, and 279,839 records with current country, historical country, and subregion information, respectively. The second method was the use of a heuristic based on common address field structures to identify “location strings” toward the end of address fields that likely represent countries, then the use of the Python pycountry module to confirm the presence of country information. This approach led to 329,025 records with country information. The third approach was to parse first author email addresses (90,799 records), recover top-level domain information, and use country code Top Level Domain (ccTLD) data from the ISO 3166 Wikipedia page to define countries (72,640 records). Only a subset of email addresses contains country information because some are from companies (.com), nonprofit organizations (.org), and others. Because a large number of records with address fields still did not have country information after taking the above 3 approaches, another approach was implemented to query address fields against a locally installed Nominatim server (v.4.2.3, https://github.com/mediagis/nominatim-docker ) using OpenStreetMap data from GEOFABRIK ( https://www.geofabrik.de/ ) to find locations. Initial testing indicated that the use of full address strings led to false positives, and the computing resource requirement for running the server was high. Thus, only location strings from the second approach that did not lead to country information were used as queries. Because multiple potential matches were returned for each query, the results were sorted based on their location importance values. The above steps led to an additional 72,401 records with country information.

Examining the overlap in country information between approaches revealed that brute force current country and pycountry searches were consistent 97.1% of the time. In addition, both approaches had high consistency with the email-based approach (92.4% and 93.9%). However, brute force subregion and Nominatim-based predictions had the lowest consistencies with the above 3 approaches (39.8% to 47.9%) and each other. Thus, a record’s country information was finalized if the information was consistent between any 2 approaches, except between the brute force subregion and Nominatim searches. This led to 330,328 records with country information.

Topical and country impact metrics

To determine annual country impact, impact scores were determined in the same way as that for annual topical impact, except that values for different countries were calculated instead of topics ( S8 Data ).

Topical preferences by country

To determine topical preference for a country C , a 2 × 2 table was established with the number of records in topic T from C , the number of records in T but not from C , the number of non- T records from C , and the number of non- T records not from C . A Fisher’s exact test was performed for each T and C combination, and the resulting p -values were corrected for multiple testing with the Bejamini–Hochberg method (see S12 Data ). The preference of T in C was defined as the degree of enrichment calculated as log likelihood ratio of values in the 2 × 2 table. Topic 5 was excluded because >50% of the countries did not have records for this topic.

The top 10 countries could be classified into a China–India cluster, an Italy–Spain cluster, and remaining countries (yellow rectangles, Fig 5E ). The clustering of Italy and Spain is partly due to similar research focusing on allergens (topic 0) and mycotoxins (topic 54) and less emphasis on gene family (topic 23) and stress tolerance (topic 28) studies ( Figs 5F and S9 ). There are also substantial differences in topical focus between countries. For example, plant science records from China tend to be enriched in hyperspectral imaging and modeling (topic 9), gene family studies (topic 23), stress biology (topic 28), and research on new plant compounds associated with herbal medicine (topic 69), but less emphasis on population genetics and evolution (topic 86, Fig 5F ). In the US, there is a strong focus on insect pest resistance (topic 75), climate, community, and diversity (topic 83), and population genetics and evolution but less focus on new plant compounds. In summary, in addition to revealing how plant science research has evolved over time, topic modeling provides additional insights into differences in research foci among different countries.

Supporting information

S1 fig. plant science record classification model performance..

(A–C) Distributions of prediction probabilities (y_prob) of (A) positive instances (plant science records), (B) negative instances (non-plant science records), and (C) positive instances with the Medical Subject Heading “Plants” (ID = D010944). The data are color coded in blue and orange if they are correctly and incorrectly predicted, respectively. The lower subfigures contain log10-transformed x axes for the same distributions as the top subfigure for better visualization of incorrect predictions. (D) Prediction probability distribution for candidate plant science records. Prediction probabilities plotted here are available in S13 Data .

https://doi.org/10.1371/journal.pbio.3002612.s001

S2 Fig. Relationships between outlier clusters and the 90 topics.

(A) Heatmap demonstrating that some outlier clusters tend to have high prediction scores for multiple topics. Each cell shows the average prediction score of a topic for records in an outlier cluster. (B) Size of outlier clusters.

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S3 Fig. Cosine similarities between topics.

(A) Heatmap showing cosine similarities between topic pairs. Top-left: hierarchical clustering of the cosine similarity matrix using the Ward algorithm. The branches are colored to indicate groups of related topics. (B) Topic labels and names. The topic ordering was based on hierarchical clustering of topics. Colored rectangles: neighboring topics with >0.5 cosine similarities.

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S4 Fig. Relative topical diversity for 20 journals.

The 20 journals with the most plant science records are shown. The journal names were taken from the journal list in PubMed ( https://www.nlm.nih.gov/bsd/serfile_addedinfo.html ).

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S5 Fig. Topical frequency and top terms during different time periods.

(A-D) Different patterns of topical frequency distributions for example topics (A) 48, (B) 35, (C) 27, and (D) 42. For each topic, the top graph shows the frequency of topical records in each time bin, which are the same as those in Fig 3 (green line), and the end date for each bin is indicated. The heatmap below each line plot depicts whether a term is among the top terms in a time bin (yellow) or not (blue). Blue dotted lines delineate different decades (see S5 Data for the original frequencies, S6 Data for the LOWESS fitted frequencies and the top terms for different topics/time bins).

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S6 Fig. Prevalence of records mentioning different taxonomic groups in Viridiplantae.

(A, B) Percentage of records mentioning specific taxa at the ( A) major lineage and (B) family levels. (C, D) The prevalence of taxon mentions over time at the (C) major lineage and (E) family levels. The data used for plotting are available in S9 Data .

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S7 Fig. Changes over time.

(A) Number of genera being mentioned in plant science records during different time bins (the date indicates the end date of that bin, exclusive). (B) Numbers of genera (blue) and organisms (salmon) with draft genomes available from National Center of Biotechnology Information in different years. (C) Percentage of US National Science Foundation (NSF) grants mentioning the genus Arabidopsis over time with peak percentage and year indicated. The data for (A–C) are in S9 Data . (D) Number of plant science records in the top 17 plant science journals from the USA (red), Great Britain (GBR) (orange), India (IND) (light green), and China (CHN) (dark green) normalized against the total numbers of publications of each country over time in these 17 journals. The data used for plotting can be found in S11 Data .

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S8 Fig. Change in country impact on plant science over time.

(A, B) Difference in 2 impact metrics from 1999 to 2020 for the 10 countries with the highest number of plant science records. (A) H-index. (B) SCImago Journal Rank (SJR). (C, D) Plots show the relationships between the impact metrics (H-index in (C) , SJR in (D) ) averaged from 1999 to 2020 and the slopes of linear fits with years as the predictive variable and impact metric as the response variable for different countries (A3 country codes shown). The countries with >400 records and with <10% missing impact values are included. The data used for plotting can be found in S11 Data .

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S9 Fig. Country topical preference.

Enrichment scores (LLR, log likelihood ratio) of topics for each of the top 10 countries. Red: overrepresentation, blue: underrepresentation. The data for plotting can be found in S12 Data .

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S1 Data. Summary of source journals for plant science records, prediction models, and top Tf-Idf features.

Sheet–Candidate plant sci record j counts: Number of records from each journal in the candidate plant science corpus (before classification). Sheet—Plant sci record j count: Number of records from each journal in the plant science corpus (after classification). Sheet–Model summary: Model type, text used (txt_flag), and model parameters used. Sheet—Model performance: Performance of different model and parameter combinations on the validation data set. Sheet–Tf-Idf features: The average SHAP values of Tf-Idf (Term frequency-Inverse document frequency) features associated with different terms. Sheet–PubMed number per year: The data for PubMed records in Fig 1A . Sheet–Plant sci record num per yr: The data for the plant science records in Fig 1A .

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S2 Data. Numbers of records in topics identified from preliminary topic models.

Sheet–Topics generated with a model based on BioBERT embeddings. Sheet–Topics generated with a model based on distilBERT embeddings. Sheet–Topics generated with a model based on SciBERT embeddings.

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S3 Data. Final topic model labels and top terms for topics.

Sheet–Topic label: The topic index and top 10 terms with the highest cTf-Idf values. Sheets– 0 to 89: The top 50 terms and their c-Tf-Idf values for topics 0 to 89.

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S4 Data. UMAP representations of different topics.

For a topic T , records in the UMAP graph are colored red and records not in T are colored gray.

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S5 Data. Temporal relationships between published documents projected onto 2D space.

The 2D embedding generated with UMAP was used to plot document relationships for each year. The plots from 1975 to 2020 were compiled into an animation.

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S6 Data. Timestamps and dates for dynamic topic modeling.

Sheet–bin_timestamp: Columns are: (1) order index; (2) bin_idx–relative positions of bin labels; (3) bin_timestamp–UNIX time in seconds; and (4) bin_date–month/day/year. Sheet–Topic frequency per timestamp: The number of documents in each time bin for each topic. Sheets–LOWESS fit 0.1/0.2/0.3: Topic frequency per timestamp fitted with the fraction parameter of 0.1, 0.2, or 0.3. Sheet—Topic top terms: The top 5 terms for each topic in each time bin.

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S7 Data. Locally weighted scatterplot smoothing (LOWESS) of topical document frequencies over time.

There are 90 scatter plots, one for each topic, where the x axis is time, and the y axis is the document frequency (blue dots). The LOWESS fit is shown as orange points connected with a green line. The category a topic belongs to and its order in Fig 3 are labeled on the top left corner. The data used for plotting are in S6 Data .

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S8 Data. The 4 criteria used for sorting topics.

Peak: the time when the LOWESS fit of the frequencies of a topic reaches maximum. 1st_reach_thr: the time when the LOWESS fit first reaches a threshold of 60% maximal frequency (peak value). Trend: upward (1), no change (0), or downward (−1). Stable: whether a topic belongs to the stable category (1) or not (0).

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S9 Data. Change in taxon record numbers and genome assemblies available over time.

Sheet–Genus: Number of records mentioning a genus during different time periods (in Unix timestamp) for the top 100 genera. Sheet–Genus: Number of records mentioning a family during different time periods (in Unix timestamp) for the top 100 families. Sheet–Genus: Number of records mentioning an order during different time periods (in Unix timestamp) for the top 20 orders. Sheet–Species levels: Number of records mentioning 12 selected taxonomic levels higher than the order level during different time periods (in Unix timestamp). Sheet–Genome assembly: Plant genome assemblies available from NCBI as of October 28, 2022. Sheet–Arabidopsis NSF: Absolute and normalized numbers of US National Science Foundation funded proposals mentioning Arabidopsis in proposal titles and/or abstracts.

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S10 Data. Taxon topical preference.

Sheet– 5 genera LLR: The log likelihood ratio of each topic in each of the top 5 genera with the highest numbers of plant science records. Sheets– 5 genera: For each genus, the columns are: (1) topic; (2) the Fisher’s exact test p -value (Pvalue); (3–6) numbers of records in topic T and in genus X (n_inT_inX), in T but not in X (n_inT_niX), not in T but in X (n_niT_inX), and not in T and X (n_niT_niX) that were used to construct 2 × 2 tables for the tests; and (7) the log likelihood ratio generated with the 2 × 2 tables. Sheet–corrected p -value: The 4 values for generating LLRs were used to conduct Fisher’s exact test. The p -values obtained for each country were corrected for multiple testing.

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S11 Data. Impact metrics of countries in different years.

Sheet–country_top25_year_count: number of total publications and publications per year from the top 25 countries with the most plant science records. Sheet—country_top25_year_top17j: number of total publications and publications per year from the top 25 countries with the highest numbers of plant science records in the 17 plant science journals used as positive examples. Sheet–prank: Journal percentile rank scores for countries (3-letter country codes following https://www.iban.com/country-codes ) in different years from 1999 to 2020. Sheet–sjr: Scimago Journal rank scores. Sheet–hidx: H-Index scores. Sheet–cite: Citation scores.

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S12 Data. Topical enrichment for the top 10 countries with the highest numbers of plant science publications.

Sheet—Log likelihood ratio: For each country C and topic T, it is defined as log((a/b)/(c/d)) where a is the number of papers from C in T, b is the number from C but not in T, c is the number not from C but in T, d is the number not from C and not in T. Sheet: corrected p -value: The 4 values, a, b, c, and d, were used to conduct Fisher’s exact test. The p -values obtained for each country were corrected for multiple testing.

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S13 Data. Text classification prediction probabilities.

This compressed file contains the PubMed ID (PMID) and the prediction probabilities (y_pred) of testing data with both positive and negative examples (pred_prob_testing), plant science candidate records with the MeSH term “Plants” (pred_prob_candidates_with_mesh), and all plant science candidate records (pred_prob_candidates_all). The prediction probability was generated using the Word2Vec text classification models for distinguishing positive (plant science) and negative (non-plant science) records.

https://doi.org/10.1371/journal.pbio.3002612.s022

Acknowledgments

We thank Maarten Grootendorst for discussions on topic modeling. We also thank Stacey Harmer, Eva Farre, Ning Jiang, and Robert Last for discussion on their respective research fields and input on how to improve this study and Rudiger Simon for the suggestion to examine differences between countries. We also thank Mae Milton, Christina King, Edmond Anderson, Jingyao Tang, Brianna Brown, Kenia Segura Abá, Eleanor Siler, Thilanka Ranaweera, Huan Chen, Rajneesh Singhal, Paulo Izquierdo, Jyothi Kumar, Daniel Shiu, Elliott Shiu, and Wiggler Catt for their good ideas, personal and professional support, collegiality, fun at parties, as well as the trouble they have caused, which helped us improve as researchers, teachers, mentors, and parents.

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A transposable element insertion is associated with an alternative life history strategy

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Immune cells are shaped by the tissue environment, yet the states of healthy human T cells are mainly studied in the blood. Here, the authors perform single cell RNA-seq of T cells from tissues and blood of healthy donors and show its utility as a reference map for comparison of human T cell states in disease.

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Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

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Novel approach reveals genomic landscapes of single-strand DNA breaks with nucleotide resolution in human cells

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What Is A Good Biology Research Topic?

Tips For Choosing A Biology Research Topics

1. Choose What Interests You

2. Select a Significant Topic

3. Check If It’s Doable

4. Add Your Unique Perspective

5. Seek Guidance

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