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DrBioRight 2.0:一款由大型语言模型驱动的用于大规模癌症功能蛋白质组学分析的生物信息学聊天机器人。

DrBioRight 2.0: an LLM-powered bioinformatics chatbot for large-scale cancer functional proteomics analysis.

作者信息

Liu Wei, Li Jun, Tang Yitao, Zhao Yining, Liu Chaozhong, Song Meiyi, Ju Zhenlin, Kumar Shwetha V, Lu Yiling, Akbani Rehan, Mills Gordon B, Liang Han

机构信息

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Houston, Houston, TX, USA.

出版信息

Nat Commun. 2025 Mar 6;16(1):2256. doi: 10.1038/s41467-025-57430-4.

Abstract

Functional proteomics provides critical insights into cancer mechanisms, facilitating the discovery of novel biomarkers and therapeutic targets. We have developed a comprehensive cancer functional proteomics resource using reverse phase protein arrays, incorporating data from nearly 8000 patient samples from The Cancer Genome Atlas and approximately 900 samples from the Cancer Cell Line Encyclopedia. Our dataset includes a curated panel of  nearly 500 high-quality antibodies, covering all major cancer hallmark pathways. To enhance the accessibility and analytic power of this resource, we introduce DrBioRight 2.0 ( https://drbioright.org ), an intuitive bioinformatic platform powered by state-of-the-art large language models. DrBioRight enables researchers to explore protein-centric cancer omics data, perform advanced analyses, visualize results, and engage in interactive discussions using natural language. By streamlining complex proteogenomic analyses, this tool accelerates the translation of large-scale functional proteomics data into meaningful biomedical insights.

摘要

功能蛋白质组学为癌症机制提供了关键见解,有助于发现新的生物标志物和治疗靶点。我们利用反相蛋白质阵列开发了一个全面的癌症功能蛋白质组学资源,整合了来自癌症基因组图谱的近8000份患者样本以及癌症细胞系百科全书的约900份样本的数据。我们的数据集包括精心挑选的近500种高质量抗体,涵盖所有主要的癌症标志性通路。为了提高该资源的可及性和分析能力,我们推出了DrBioRight 2.0(https://drbioright.org),这是一个由最先进的大语言模型驱动的直观生物信息学平台。DrBioRight使研究人员能够探索以蛋白质为中心的癌症组学数据,进行高级分析,可视化结果,并使用自然语言进行交互式讨论。通过简化复杂的蛋白质基因组分析,该工具加速了将大规模功能蛋白质组学数据转化为有意义的生物医学见解的过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2343/11885830/d82712c5ee4a/41467_2025_57430_Fig1_HTML.jpg

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