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

Author

Listed:
  • 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
    The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Houston)

  • 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
    The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Houston
    The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center)

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.

Suggested Citation

  • Wei Liu & Jun Li & Yitao Tang & Yining Zhao & Chaozhong Liu & Meiyi Song & Zhenlin Ju & Shwetha V. Kumar & Yiling Lu & Rehan Akbani & Gordon B. Mills & Han Liang, 2025. "DrBioRight 2.0: an LLM-powered bioinformatics chatbot for large-scale cancer functional proteomics analysis," Nature Communications, Nature, vol. 16(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57430-4
    DOI: 10.1038/s41467-025-57430-4
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    References listed on IDEAS

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    1. Jordi Barretina & Giordano Caponigro & Nicolas Stransky & Kavitha Venkatesan & Adam A. Margolin & Sungjoon Kim & Christopher J.Wilson & Joseph Lehár & Gregory V. Kryukov & Dmitriy Sonkin & Anupama Red, 2012. "Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity," Nature, Nature, vol. 492(7428), pages 290-290, December.
    2. Jordi Barretina & Giordano Caponigro & Nicolas Stransky & Kavitha Venkatesan & Adam A. Margolin & Sungjoon Kim & Christopher J. Wilson & Joseph Lehár & Gregory V. Kryukov & Dmitriy Sonkin & Anupama Re, 2012. "The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity," Nature, Nature, vol. 483(7391), pages 603-607, March.
    3. Xin Jin & Zelalem Demere & Karthik Nair & Ahmed Ali & Gino B. Ferraro & Ted Natoli & Amy Deik & Lia Petronio & Andrew A. Tang & Cong Zhu & Li Wang & Danny Rosenberg & Vamsi Mangena & Jennifer Roth & K, 2020. "A metastasis map of human cancer cell lines," Nature, Nature, vol. 588(7837), pages 331-336, December.
    4. Mathew J. Garnett & Elena J. Edelman & Sonja J. Heidorn & Chris D. Greenman & Anahita Dastur & King Wai Lau & Patricia Greninger & I. Richard Thompson & Xi Luo & Jorge Soares & Qingsong Liu & Francesc, 2012. "Systematic identification of genomic markers of drug sensitivity in cancer cells," Nature, Nature, vol. 483(7391), pages 570-575, March.
    5. Rehan Akbani & Patrick Kwok Shing Ng & Henrica M. J. Werner & Maria Shahmoradgoli & Fan Zhang & Zhenlin Ju & Wenbin Liu & Ji-Yeon Yang & Kosuke Yoshihara & Jun Li & Shiyun Ling & Elena G. Seviour & Pr, 2014. "A pan-cancer proteomic perspective on The Cancer Genome Atlas," Nature Communications, Nature, vol. 5(1), pages 1-15, September.
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