Liu-Hy/GenoMAS
A minimalist multi-agent framework for rubost automation of scientific analysis workflows, such as gene expression analysis.
This project automates complex gene expression analysis workflows, designed for scientists in computational genomics and bioinformatics. It takes raw transcriptomic datasets, such as those from GEO or TCGA, and automatically identifies significant genes related to specific traits, while accounting for confounding factors. The output consists of biologically meaningful gene-trait associations, including both known and potentially novel findings for further investigation.
133 stars. No commits in the last 6 months.
Use this if you need to robustly and automatically analyze large gene expression datasets to discover gene-trait associations without extensive manual coding or debugging.
Not ideal if you require fine-grained, step-by-step manual control over every piece of code generated or if your primary interest is in developing new LLM agent architectures rather than applying them to genomics.
Stars
133
Forks
24
Language
Python
License
MIT
Category
Last pushed
Oct 10, 2025
Commits (30d)
0
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