Large Language Models for Accessible Reporting of Bioinformatics Analyses in Interdisciplinary Contexts.

ABSTRACT: Health and life scientists routinely collaborate with quantitative scientists for data analysis and interpretation, yet miscommunication often obscures the interpretation of complex results. Large Language Models (LLMs) offer a promising way to bridge this gap, but their cross-discipline interpretative skill remains limited on real-word bioinformatics analyses. We therefore benchmarked four state-of-the-art LLMs: GPT-4o, o1, Claude 3.7 Sonnet, and Gemini 2.0 Flash, using automated and human evaluation frameworks to ensure holistic evaluation. Automated assessment employed multiple choice questions designed using Bloom's taxonomy to assess multiple levels of understanding, while human evaluation tasked scientists to score summaries for factual consistency, lack of harmfulness, comprehensive

SUBMITTER: Yu L 

PROVIDER: S-EPMC12642402 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature