Aims and scope

BioData Mining is an open access, open peer-reviewed, informatics journal encompassing research on all aspects of Artificial Intelligence (AI), Machine Learning, and Visual Analytics, applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, genomic, metabolomic data and/or electronic health records, social determinants of health, and environmental exposure data.

Topical areas include, but are not limited to:

    Development, evaluation, and application of novel data mining and machine learning algorithms.
    Adaptation, evaluation, and application of traditional data mining and machine learning algorithms.
    Open-source software for the application of data mining and machine learning algorithms.
    Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies.
    Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.

Data types include

    Imaging
    Electronic health records
    Biobanks
    Environmental data
    Social and behavioral data
    Wearable devices
    Social media data