Abstract 6303: DrBioRight: an AI-driven chatbot for cancer data analysis
We present DrBioRight, a cutting-edge chatbot designed to streamline omics data analysis by employing large language models (LLMs). Modern omics technologies generate extensive cancer omics datasets, but conventional software often requires significant technical expertise. DrBioRight addresses this challenge by enabling researchers to interact with the platform in a conversational manner, simplifying complex analytical workflows. DrBioRight's chatbot-driven interface allows users to search datasets, perform data analysis, access insights, and generate visualizations by posing natural language questions or commands. This intuitive approach eliminates the need for programming knowledge, making omics research more accessible. Key features include LLM-powered conversational capabilities, real-time task prediction and guidance, collaborative functionalities for research teams, and integration of new tools and datasets via community contributions. By transforming the interaction model from technical to conversational, DrBioRight serves as an intelligent research assistant that democratizes omics data analysis, enhances collaboration, and accelerates scientific discoveries in cancer research. Citation Format: Han Liang, Jun Li, Wei Liu. DrBioRight: an AI-driven chatbot for cancer data analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6303.
Authors
- Han Liang (ORCID: https://orcid.org/0000-0001-7633-286X)
- Jun Li (ORCID: https://orcid.org/0000-0002-1171-7141)
- Wei Liu (ORCID: https://orcid.org/0000-0003-0935-3999)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-6303
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00