Harnessing large language models (LLMs) for candidate gene prioritization and selection.

ABSTRACT:

Background

Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.

Methods

In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2

SUBMITTER: Toufiq M 

PROVIDER: S-EPMC10580627 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature