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 , 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.
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.
The era of artificial-intelligence chatbots that seem to understand and use language the way we humans do has begun. Under the hood, these chatbots use large language models, a particular kind of ...
Medical Topics Genes Life Sciences Biotechnology And Bioengineering CRISPR Gene Editing Computer Modeling Artificial Intelligence Computer Programming
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