Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis.

Zhizheng Wang, Yifan Yang, Qiao Jin, Zhiyong Lu

Abstract readPreprint

In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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Updated by
Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis.2026

5 · Who and what money

Authors and funding

4 authors.

Zhizheng WangDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Yifan YangDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Qiao JinDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Zhiyong LuDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The gene set analysis (GSA) is a foundational approach for uncovering the molecular functions associated with a group of genes. Recently, LLM-powered methods have emerged to annotate gene sets with biological functions together with coherent explanatory insights. However, existing studies primarily focus on proprietary models, which have been shown to outperform their open-source counterparts despite concerns over cost and data privacy. Furthermore, no research has investigated the application of advanced reasoning strategies to the GSA task. To address this gap, we introduce

Indexed as

Data AugmentationFine-tuningGene Set AnalysisLLMReasoning

Identifiers

PMID41479449

PMCPMC12754708

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.