“Biomedical data” refers to the increasingly large corpus of machine-mineable data encompassing two similar, yet pointedly distinct fields: biology and medicine. In recent years, experimental and technological advancements in these fields have resulted in an unprecedented diversity of molecular omics data and longitudinal health record data available for analysis. Moreover, entirely new data sources such as social networking data, wearable technologies and environmental measurements have emerged and are relevant indicators of phenomena observed across both biology and medicine. Creative and sophisticated integration of these datasets promises the opportunity to further biological knowledge and understanding of disease and ultimately to advance our ability to more holistically detect and treat disease and improve patient care. However, challenges stemming from limited data quality and standardization coupled with a dramatic increase in data size and required computational resources arise in pursuit of these goals. In this session, we are particularly interested in innovative approaches that utilize new or new combinations of biomedical data sources to address previously intractable questions. We will focus specifically on cutting-edge methods aimed at pattern discovery in biomedical data.