Work

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

Year: 2024

Type: conference-paper

Abstract: 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.

Source: Pacific Symposium on Biocomputing (PSB)

DOI: 10.1142/9789819807024_0051

Authors: Brett Beaulieu-Jones, Steven Brenner

Institutions: University of Chicago, University of California, Berkeley

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

Funders: National Institutes of Health, National Institute of Neurological Disorders and Stroke

Award: G3357827492