Disulfidptosis-associated gene signatures in sepsis: a diagnostic model based on an LLM-assisted bioinformatics analysis.

ABSTRACT:

Purpose

This study investigated the involvement of disulfidptosis in the pathophysiology of sepsis by applying a bioinformatics analysis assisted by large language models (LLMs).

Methods

Based on DeepSeek R1 and retrieval-augmented generation technology, a deep retrieval architecture was developed for extracting disulfidptosis-related genes. An intersection of genes from LLM extraction, manual extraction, and datasets was included for bioinformatics analyses. Using DeepSeek R1, we synthesized a multi-step bioinformatics protocol from prior publications. The analyses were then performed according to the protocol. Key gene candidates were identified using multiple machine learning models, and validation was performed in a cecal ligation and puncture mouse model of sepsis.

R

SUBMITTER: Liu T 

PROVIDER: S-EPMC12618752 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature