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

Wei Liu, Jun Li, Yitao Tang, Yining Zhao, Chaozhong Liu, Meiyi Song, Zhenlin Ju, Shwetha V. Kumar, Yiling Lu, Rehan Akbani, Gordon B. Mills and Han Liang ()
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Wei Liu: The University of Texas MD Anderson Cancer Center
Jun Li: The University of Texas MD Anderson Cancer Center
Yitao Tang: The University of Texas MD Anderson Cancer Center
Yining Zhao: The University of Texas MD Anderson Cancer Center
Chaozhong Liu: Baylor College of Medicine
Meiyi Song: Brown University
Zhenlin Ju: The University of Texas MD Anderson Cancer Center
Shwetha V. Kumar: The University of Texas MD Anderson Cancer Center
Yiling Lu: The University of Texas MD Anderson Cancer Center
Rehan Akbani: The University of Texas MD Anderson Cancer Center
Gordon B. Mills: Oregon Health & Science University
Han Liang: The University of Texas MD Anderson Cancer Center

Nature Communications, 2025, vol. 16, issue 1, 1-6

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-57430-4

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