AI Language Models Show Promise in Automating Functional Genomics Research

Large language models could help automate functional genomics research

University of California - San DiegoDec 2 2024 Researchers at University of California San Diego School of Medicine have demonstrated that large language models (LLMs), such as GPT-4, could help automate functional genomics research, which seeks to determine what genes do and how they interact. The most frequently-used approach in functional genomics, called gene set enrichment, aims to determine the function of experimentally-identified gene sets by comparing them to existing genomics databases. However, more interesting and novel biology is often beyond the scope of established databases. Using artificial intelligence (AI) to analyze gene sets could save scientists many hours of intensive labor and bring science one step closer to automating one of the most widely used methods for understanding how genes work together to influence biology. Testing five different LLMs, the researchers found that GPT-4 was the most successful, achieving a 73% accuracy rate in identifying common functions of curated gene sets from a commonly used genomics database. When asked to analyze random gene sets, GPT-4 refused to provide a name in 87% of cases, demonstrating the potential of GPT-4 to analyze gene sets with minimal hallucination. GPT-4 was also capable of providing detailed narratives to support its naming process. While further research is needed to fully explore the potential of LLMs in automating functional genomics, the study highlights the need for continued investment in the development of LLMs and their applications in genomics and precision medicine. To support this, the researchers created a web portal to help other researchers incorporate LLMs into their functional genomics workflows. More broadly, the findings also demonstrate the power of AI to revolutionize the scientific process by synthesizing complex information to generate new, testable hypotheses in a fraction of the time. The study, published in Nature Methods, was led by Trey Ideker, Ph.D., a professor at UC San Diego School of Medicine and UC San Diego Jacobs School of Engineering, Dexter Pratt, Ph.D., a software architect in Ideker's group, and Clara Hu, a biomedical sciences doctoral candidate in Ideker's group. The study was funded, in part, by the National Institutes of Health. University of California - San Diego Journal reference: Hu, M., et al. (2024). Evaluation of large language models for discovery of gene set function. Nature Methods. doi.org/10.1038/s41592-024-02525-x.

How AI could automate genomics research

Researchers at University of California San Diego School of Medicine have demonstrated that large language models (LLMs), such as GPT-4, could help automate functional genomics research, which seeks to determine what genes do and how they interact. The work is published in the journal Nature Methods. The most frequently-used approach in functional genomics, called gene set enrichment, aims to determine the function of experimentally-identified gene sets by comparing them to existing genomics databases. However, more interesting and novel biology is often beyond the scope of established databases. Using artificial intelligence (AI) to analyze gene sets could save scientists many hours of intensive labor and bring science one step closer to automating one of the most widely used methods for understanding how genes work together to influence biology. Testing five different LLMs, the researchers found that GPT-4 was the most successful, achieving a 73% accuracy rate in identifying common functions of curated gene sets from a commonly used genomics database. When asked to analyze random gene sets, GPT-4 refused to provide a name in 87% of cases, demonstrating the potential of GPT-4 to analyze gene sets with minimal hallucination. GPT-4 was also capable of providing detailed narratives to support its naming process. While further research is needed to fully explore the potential of LLMs in automating functional genomics, the study highlights the need for continued investment in the development of LLMs and their applications in genomics and precision medicine. To support this, the researchers created a web portal to help other researchers incorporate LLMs into their functional genomics workflows. More broadly, the findings also demonstrate the power of AI to revolutionize the scientific process by synthesizing complex information to generate new, testable hypotheses in a fraction of the time. The study was led by Trey Ideker, Ph.D., a professor at UC San Diego School of Medicine and UC San Diego Jacobs School of Engineering, Dexter Pratt, Ph.D., a software architect in Ideker's group, and Clara Hu, a biomedical sciences doctoral candidate in Ideker's group.

How Artificial Intelligence Could Automate Genomic | Newswise

Functional genomics seeks to identify what genes do and how they interact. Researchers at UC San Diego have demonstrated that large language models such as GPT-4 could make functional genomics research significantly faster and less laborious than current, non-AI approaches. Newswise -- Researchers at University of California San Diego School of Medicine have demonstrated that large language models (LLMs), such as GPT-4, could help automate functional genomics research, which seeks to determine what genes do and how they interact. The most frequently-used approach in functional genomics, called gene set enrichment, aims to determine the function of experimentally-identified gene sets by comparing them to existing genomics databases. However, more interesting and novel biology is often beyond the scope of established databases. Using artificial intelligence (AI) to analyze gene sets could save scientists many hours of intensive labor and bring science one step closer to automating one of the most widely used methods for understanding how genes work together to influence biology. Testing five different LLMs, the researchers found that GPT-4 was the most successful, achieving a 73% accuracy rate in identifying common functions of curated gene sets from a commonly used genomics database. When asked to analyze random gene sets, GPT-4 refused to provide a name in 87% of cases, demonstrating the potential of GPT-4 to analyze gene sets with minimal hallucination. GPT-4 was also capable of providing detailed narratives to support its naming process. While further research is needed to fully explore the potential of LLMs in automating functional genomics, the study highlights the need for continued investment in the development of LLMs and their applications in genomics and precision medicine. To support this, the researchers created a web portal to help other researchers incorporate LLMs into their functional genomics workflows. More broadly, the findings also demonstrate the power of AI to revolutionize the scientific process by synthesizing complex information to generate new, testable hypotheses in a fraction of the time. The study, published in Nature Methods, was led by Trey Ideker, Ph.D., a professor at UC San Diego School of Medicine and UC San Diego Jacobs School of Engineering, Dexter Pratt, Ph.D., a software architect in Ideker's group, and Clara Hu, a biomedical sciences doctoral candidate in Ideker's group. The study was funded, in part, by the National Institutes of Health.

How artificial intelligence could automate genomics research

New research suggests that large language models like GPT-4 could streamline the process of gene set enrichment, an approach what genes do and how they interact. Results bring science one step closer to automating one of the most widely used methods in genomics research. Researchers at University of California San Diego School of Medicine have demonstrated that large language models (LLMs), such as GPT-4, could help automate functional genomics research, which seeks to determine what genes do and how they interact. The most frequently-used approach in functional genomics, called gene set enrichment, aims to determine the function of experimentally-identified gene sets by comparing them to existing genomics databases. However, more interesting and novel biology is often beyond the scope of established databases. Using artificial intelligence (AI) to analyze gene sets could save scientists many hours of intensive labor and bring science one step closer to automating one of the most widely used methods for understanding how genes work together to influence biology.