Bioinformatics and Biomedical Informatics Next-generation sequencing (NGS) data storage and analysis Large scale biological network construction and learning Population-based bioinformatics Genome structural change detection Large-scale bio-image and medical-image analysis Big data in molecular simulation and protein structure prediction Big data in systems biology Big data in precision medicine and stratified medicine Big data in drug discovery, development, and post-market surveillance Big data in semantics and bio-text mining Healthcare Systems Real-time aspects of healthcare data infrastructure Security and privacy for clinical data in big data infrastructures Health IT implementations and demonstrations Case studies for healthcare analysis in distributed environments Benchmarking of big data infrastructure in healthcare Novel data analysis algorithms that enable integrated discovery of knowledge from structured and unstructured Electronic Medical Records (EMR) Analysis and visualizing for summarizing large patient data in EMRs Novel algorithms and applications dealing with noisy, incomplete, but large EMR data Integrating genomic data in today’s medicine to improve human health Data science and modeling for health analysis Advances in new storage models for data variety (records, images, Magnetic Resonance Imaging (MRI), scans) for hospitals Big data challenges in accountable care settings Extracting meaning from multi-structured big data in real time to improve outcome Combining information from imaging (RIS, PACS), Electronic Health Records (EHR), laboratories, genomics to give coherent diagnosis and treatment Leveraging social networks for data aggregation Smart visualizations for big data streams Analysis of big data from home monitoring devices Design patterns and anti-patterns for development of solutions for big data Analysis of Big Medical Data Real-time analysis of big medical data in the course of precision medicine Analysis of longitudinal and time-series data to discover new correlations Co-registration of patient data acquired over several time-points in their life Identification of important metadata that has to be tracked over a longitudinal duration Software platforms for enabling easy access to the patient’s medical and clinical history Gap-handling in history-taking Quality improvement and noise-handling on longitudinal data Missing functionality in current clinical decision support systems using longitudinal data