Automating Candidate Gene Prioritization with Large Language Models: Development and Benchmarking of an API-Driven Workflow Leveraging GPT-4

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Dades bibliogràfiques
Publicat a:	bioRxiv (Dec 16, 2024)
Autor principal:	Khan, Taushif
Altres autors:	Toufiq, Mohammed, Yurieva, Marina, Indrawattana, Nitaya, Jittmittraphap, Akanitt, Kosoltanapiwat, Nathamon, Pumirat, Pornpan, Sukphopetch, Passanesh, Vanaporn, Muthita, Kaber, Basirudeen, Palucka, Karolina, Rinchai, Darawan, Chaussabel, Damien
Publicat:	Cold Spring Harbor Laboratory Press
Matèries:	Sepsis Application programming interface Models Automation Genes Large language models Gene clusters
Accés en línia:	Citation/Abstract Full Text - PDF Full text outside of ProQuest
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Descripció
Resum:	In this exploratory study, we developed an automated workflow that leverages Large Language Models, specifically GPT-4, to prioritize candidate genes for targeted assay development. The workflow automates interaction with OpenAI models and enables prompt creation, submission. It features customizable prompts designed to evaluate candidate genes based on criteria such as association with biological processes, biomarker potential, and therapeutic implications, which can be tailored for specific diseases or processes. Benchmarking experiments comparing the performance of the Application Programming Interface (API)-based automated prompting approach with manual prompting demonstrated high consistency and reproducibility in gene prioritization results. The automated method exhibited scalability by successfully prioritizing genes relevant to sepsis from the BloodGen3 repertoire, comprising 11,465 genes, distributed among 382 modules. The workflow efficiently identified sepsis-associated genes across the repertoire, revealing distinct gene clusters and providing insights into their distribution within module aggregates and individual modules. This proof-of-concept study demonstrates how LLMs can enhance gene prioritization, streamlining the identification process for targeted assays across various biological contexts. However, it also reveals the need for further validation and highlights the exploratory nature of this work due to scoring inconsistencies and the necessity for manual fact-checking. Despite these challenges, the automated workflow holds promise for accelerating targeted assay development for disease management and paves the way for future research.Competing Interest StatementThe authors have declared no competing interest.
ISSN:	2692-8205
DOI:	10.1101/2024.12.10.627808
Font:	Biological Science Database