GenoMAS: A Multi-Agent Framework for Scientific Discovery via
  Code-Driven Gene Expression Analysis
Gene expression analysis holds the key to many biomedical discoveries, yet
extracting insights from raw transcriptomic data remains formidable due to the
complexity of multiple large, semi-structured files and the need for extensive
domain expertise. Current automation approaches are often limited by either
inflexible workflows that break down in edge cases or by fully autonomous
agents that lack the necessary precision for rigorous scientific inquiry.
GenoMAS charts a different course by presenting a team of LLM-based scientists
that integrates the reliability of structured workflows with the adaptability
of autonomous agents. GenoMAS orchestrates six specialized LLM agents through
typed message-passing protocols, each contributing complementary strengths to a
shared analytic canvas. At the heart of GenoMAS lies a guided-planning
framework: programming agents unfold high-level task guidelines into Action
Units and, at each juncture, elect to advance, revise, bypass, or backtrack,
thereby maintaining logical coherence while bending gracefully to the
idiosyncrasies of genomic data.
  On the GenoTEX benchmark, GenoMAS reaches a Composite Similarity Correlation
of 89.13% for data preprocessing and an F_1 of 60.48% for gene
identification, surpassing the best prior art by 10.61% and 16.85%
respectively. Beyond metrics, GenoMAS surfaces biologically plausible
gene-phenotype associations corroborated by the literature, all while adjusting
for latent confounders. Code is available at https://github.com/Liu-Hy/GenoMAS.
68886a70af872d625c10c6b5
Haoyang Liu
claimed_verified
2025-07-29T07:07:27.474Z
67e894cc0afe9169e8bac4aa
https://cdn-avatars.huggingface.co/v1/production/uploads/67e894cc0afe9169e8bac4aa/pnGZMwKlil9dpOb91cbEs.jpeg
Haoyang Liu
user
Liu-Hy
68886a70af872d625c10c6b6
Yijiang Li
claimed_verified
2025-09-05T14:17:11.311Z
6419309f22270b3ccf177c77
https://cdn-avatars.huggingface.co/v1/production/uploads/6419309f22270b3ccf177c77/KQa1586iBBKqucUlfpuPp.jpeg
William Li
user
williamium
68886a70af872d625c10c6b7
Haohan Wang
GenoMAS: A Multi-Agent Framework for Scientific Discovery via
  Code-Driven Gene Expression Analysis
Gene expression analysis holds the key to many biomedical discoveries, yet
extracting insights from raw transcriptomic data remains formidable due to the
complexity of multiple large, semi-structured files and the need for extensive
domain expertise. Current automation approaches are often limited by either
inflexible workflows that break down in edge cases or by fully autonomous
agents that lack the necessary precision for rigorous scientific inquiry.
GenoMAS charts a different course by presenting a team of LLM-based scientists
that integrates the reliability of structured workflows with the adaptability
of autonomous agents. GenoMAS orchestrates six specialized LLM agents through
typed message-passing protocols, each contributing complementary strengths to a
shared analytic canvas. At the heart of GenoMAS lies a guided-planning
framework: programming agents unfold high-level task guidelines into Action
Units and, at each juncture, elect to advance, revise, bypass, or backtrack,
thereby maintaining logical coherence while bending gracefully to the
idiosyncrasies of genomic data.
  On the GenoTEX benchmark, GenoMAS reaches a Composite Similarity Correlation
of 89.13% for data preprocessing and an F_1 of 60.48% for gene
identification, surpassing the best prior art by 10.61% and 16.85%
respectively. Beyond metrics, GenoMAS surfaces biologically plausible
gene-phenotype associations corroborated by the literature, all while adjusting
for latent confounders. Code is available at https://github.com/Liu-Hy/GenoMAS.
GenoMAS, a team of LLM-based agents, integrates structured workflows and autonomous adaptability for gene expression analysis, achieving high performance on benchmarks and uncovering biologically plausible associations.
LLM-based scientists typed message-passing protocols guided-planning framework Action Units Composite Similarity Correlation F$_1$ score gene-phenotype associations latent confounders