Leveraging Foundational Models in Computational Biology: Validation, Understanding, and Innovation

Large Language Models (LLMs) have shown significant promise across a wide array of fields, including biomedical research, but face notable limitations in their current applications. While they offer a new paradigm for data analysis and hypothesis generation, their efficacy in computational biology t...

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Bibliographic Details
Published inPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Vol. 30; p. 702
Main Authors Beaulieu-Jones, Brett, Brenner, Steven
Format Journal Article
LanguageEnglish
Published United States 2025
Subjects
Computational Biology
Humans
Natural Language Processing
Validation Studies as Topic
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Summary:Large Language Models (LLMs) have shown significant promise across a wide array of fields, including biomedical research, but face notable limitations in their current applications. While they offer a new paradigm for data analysis and hypothesis generation, their efficacy in computational biology trails other applications such as natural language processing. This workshop addresses the state of the art in LLMs, discussing their challenges and the potential for future development tailored to computational biology. Key issues include difficulties in validating LLM outputs, proprietary model limitations, and the need for expertise in critical evaluation of model failure modes.
ISSN:2335-6936
DOI:10.1142/9789819807024_0051