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利用文献增强的大语言模型推断癌症中的药物-基因关系

Inferring Drug-Gene Relationships in Cancer Using Literature-Augmented Large Language Models.

作者信息

Lai Ying-Ju, Wang Li-Ju, Yasaka Tyler M, Shin Yuna, Ning Michael, Tan Yanhao, Shih Chien-Hung, Guo Yibing, Chen Po-Yuan, Galloway Hugh, Liu Zhentao, Das Arun, Tseng George C, Monga Satdarshan P, Huang Yufei, Chiu Yu-Chiao

机构信息

UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.

Department of Biostatistics and Health Data Science, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania.

出版信息

Cancer Res Commun. 2025 Apr 1;5(4):706-718. doi: 10.1158/2767-9764.CRC-25-0030.

DOI:10.1158/2767-9764.CRC-25-0030
PMID:40293950
原文链接:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12036822/
Abstract

This study presents a novel approach that integrates LLMs with real-time biomedical literature to uncover drug-gene relationships, transforming how cancer researchers identify therapeutic targets, repurpose drugs, and interpret complex molecular interactions. GeneRxGPT, our user-friendly tool, enables researchers to leverage this approach without requiring computational expertise.

摘要

本研究提出了一种将大语言模型与实时生物医学文献相结合的新方法，以揭示药物-基因关系，改变了癌症研究人员识别治疗靶点、重新利用药物以及解释复杂分子相互作用的方式。我们用户友好的工具GeneRxGPT使研究人员无需计算专业知识就能利用这种方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/b702731012d2/crc-25-0030_f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/b4b7707b715c/crc-25-0030_f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/1ab4fb8e9722/crc-25-0030_f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/701914f3cce0/crc-25-0030_f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/50bda95f7283/crc-25-0030_f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/b702731012d2/crc-25-0030_f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/b4b7707b715c/crc-25-0030_f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/1ab4fb8e9722/crc-25-0030_f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/701914f3cce0/crc-25-0030_f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/50bda95f7283/crc-25-0030_f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ea8/12036822/b702731012d2/crc-25-0030_f5.jpg

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本文引用的文献

1
Evaluation of large language models for discovery of gene set function.用于发现基因集功能的大语言模型评估
Nat Methods. 2025 Jan;22(1):82-91. doi: 10.1038/s41592-024-02525-x. Epub 2024 Nov 28.
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Development of mutated β-catenin gene signature to identify mutations from whole and spatial transcriptomic data in patients with HCC.开发突变β-连环蛋白基因特征以从肝癌患者的全转录组和空间转录组数据中识别突变。
JHEP Rep. 2024 Aug 20;6(12):101186. doi: 10.1016/j.jhepr.2024.101186. eCollection 2024 Dec.
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Suitability of GPT-4o as an evaluator of cardiopulmonary resuscitation skills examinations.
GPT-4o 作为心肺复苏技能考试评估者的适用性。
Resuscitation. 2024 Nov;204:110404. doi: 10.1016/j.resuscitation.2024.110404. Epub 2024 Sep 28.
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Detecting hallucinations in large language models using semantic entropy.使用语义熵检测大型语言模型中的幻觉。
Nature. 2024 Jun;630(8017):625-630. doi: 10.1038/s41586-024-07421-0. Epub 2024 Jun 19.
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Augmented non-hallucinating large language models as medical information curators.增强型非幻觉大语言模型作为医学信息整理者
NPJ Digit Med. 2024 Apr 23;7(1):100. doi: 10.1038/s41746-024-01081-0.
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Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis.在单细胞RNA测序分析中评估GPT-4用于细胞类型注释
Nat Methods. 2024 Aug;21(8):1462-1465. doi: 10.1038/s41592-024-02235-4. Epub 2024 Mar 25.
7
Empowering personalized pharmacogenomics with generative AI solutions.利用生成式人工智能解决方案增强个性化药物基因组学。
J Am Med Inform Assoc. 2024 May 20;31(6):1356-1366. doi: 10.1093/jamia/ocae039.
8
GeneGPT: augmenting large language models with domain tools for improved access to biomedical information.GeneGPT：利用领域工具增强大型语言模型，以改善对生物医学信息的访问。
Bioinformatics. 2024 Feb 1;40(2). doi: 10.1093/bioinformatics/btae075.
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Protect our environment from information overload.保护我们的环境免受信息过载之害。
Nat Hum Behav. 2024 Mar;8(3):402-403. doi: 10.1038/s41562-024-01833-8.
10
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EBioMedicine. 2024 Feb;100:104988. doi: 10.1016/j.ebiom.2024.104988. Epub 2024 Feb 1.