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.
topics of interest
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