{"id":2161,"date":"2026-07-27T16:23:47","date_gmt":"2026-07-27T16:23:47","guid":{"rendered":"https:\/\/sites.rutgers.edu\/precision-medicine\/?p=2161"},"modified":"2026-07-27T16:23:47","modified_gmt":"2026-07-27T16:23:47","slug":"uses-of-agentic-ai-in-biodata-mining","status":"publish","type":"post","link":"https:\/\/sites.rutgers.edu\/precision-medicine\/uses-of-agentic-ai-in-biodata-mining\/","title":{"rendered":"Uses of Agentic AI in Biodata Mining"},"content":{"rendered":"

Uses of Agentic AI in Biodata Mining<\/strong><\/a>, our latest collection in BioData Mining journal by BMC\/Springer Nature<\/a>.<\/span><\/span><\/p>\n

Agentic AI, autonomous systems capable of adaptive reasoning, planning, and decision-making, is emerging as a powerful paradigm for biodata mining. This Collection focuses on how agentic AI enhances the analysis of high-dimensional biological and biomedical datasets, including genomics, transcriptomics, epigenomics, proteomics, microbiomics, metabolomics, structural or spatial omics, comparative omics, biomedical image, electronic health records, and multimodal integrations of these. By dynamically optimizing workflows and interacting with complex data environments, agentic AI can accelerate knowledge discovery and support precision medicine.<\/p>\n

We invite contributions that demonstrate algorithmic innovations, system architectures, and practical applications of agentic AI in large-scale biological data analysis. Submissions may include open-source tools, frameworks, and case studies that improve scalability, reproducibility, interpretability, and robustness in biomedical analytics. We particularly welcome work that rigorously evaluates agentic behavior, examines human\u2013AI collaboration and oversight, and addresses safety, bias, or governance considerations relevant to biomedical and clinical contexts.<\/p>\n

Topics of interest (but not limited to):<\/strong><\/p>\n