Abstract B057: DrBioRight: an AI chat assistant enabling scalable and flexible multi-omics analysis in cancer
Bibliographic record
Abstract
Abstract Over the past decade, high-throughput omics technologies have generated massive datasets from patient tumors, cell lines, and animal models, offering deep insights into disease mechanisms. However, analyzing these data remains a major challenge for researchers lacking computational expertise. Existing tools and bioinformatics cores offer partial solutions but often fall short in flexibility, scalability, or accessibility. To address these challenges, we developed DrBioRight, an AI-powered assistant that enables natural language-based analysis of multi-omics data. By integrating large-scale omics datasets, advanced analytic/visualization tools, and large language model-based AI agents into a unified chat interface, DrBioRight allows users to ask biomedical questions and receive interpretable results in real time. Already adopted by thousands of users, the platform significantly enhances data analysis efficiency in biomedical research. DrBioRight consists of three main components: (i) a data portal featuring multi-omics datasets from thousands of clinical and preclinical samples across hundreds of patient cohorts; (ii) a tool store offering customizable analytic and visualization modules; and (iii) AI agents that interpret queries, generate code, and automate workflows. The platform also supports external data uploads and community-contributed tools through easy-to-use integration APIs. To demonstrate its utility, a user might request, “Generate a heatmap for gene expression data.” DrBioRight processes the input data, identifies the most appropriate tool, and returns an interactive heatmap with features such as gene selection, zooming, scatter views, and pathway mapping. Users can then issue follow-up requests through ongoing conversation, such as correlation analysis, survival analysis, or subgroup comparisons. The platform also supports result summarization, plot customization, and export of both data and code, enabling flexible, iterative, and reproducible biomedical research. Overall, DrBioRight enhances research efficiency by minimizing computational barriers; fosters collaboration through shared data and tools; improves the quality and interpretability of analyses; increases transparency and reproducibility; and serves as an open-access hub for integrating and disseminating omics data and bioinformatics tools. In summary, DrBioRight is a versatile, end-to-end platform that lowers barriers to omics data analysis while accelerating discovery and collaboration in cancer research. Citation Format: Jun Li, Wei Liu, Yitao Tang, Yining Zhao, Han Liang. DrBioRight: an AI chat assistant enabling scalable and flexible multi-omics analysis in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B057.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".