BioData Mining is an open access, open peer-reviewed informatics journal publishing rigorous and creative computational research at the interface of Artificial Intelligence, Machine Learning, biomedical data science, and translational informatics. The journal focuses on methodological, computational, and informatics contributions that advance how complex biological and biomedical data are represented, modeled, integrated, evaluated, interpreted, and translated into scientific or clinical insight. BioData Mining welcomes work in biology, medicine, and basic science when the computational contribution is central and broadly useful beyond a single dataset, disease, cohort, or assay. The journal prioritizes studies that introduce, adapt, evaluate, or creatively apply AI and data mining methods to high-dimensional biomedical data, especially when they demonstrate methodological rigor, thoughtful data representation, meaningful validation, interpretability, and potential for generalization. Topical areas include, but are not limited to: Development, evaluation, and application of novel machine learning, artificial intelligence, statistical, and data mining methods. Rigorous adaptation and evaluation of existing computational methods when applied in a biologically or clinically meaningful and methodologically insightful way. Foundation models, representation learning, probing, adapter methods, retrieval-augmented systems, and agentic AI approaches for biomedical discovery and translational informatics. Multimodal, longitudinal, temporal, and real-world biomedical data modeling, including approaches that address missingness, bias, confounding, calibration, uncertainty, robustness, data drift, and external validation. Open-source software, benchmarks, datasets, databases, and web services that support reproducible, reusable, and generalizable biomedical AI and data mining research. Pre-processing, post-processing, interpretation, visualization, and evaluation frameworks that improve knowledge discovery from complex biomedical data. Data types include: Imaging Electronic health records Claims and administrative data Biobanks Genomic, transcriptomic, proteomic, metabolomic, and other omics data Environmental exposure data Social determinants of health Social and behavioral data Wearable and sensor data Patient-generated data Biomedical literature, knowledge graphs, and other structured or unstructured knowledge sources.