Mathematical modeling and big data analytics in biomedicine

Abstract

The World's total data is doubling every two years. Data expansion includes growth in quantity, complexity, and types of data. The enormous rate of generation and on-line access to data is profoundly changing the way how business is conducted. Biomedical data include research and development data, clinical data, activity and cost data, patient behavior data, basic science data, and standards and ontologies, among others. Furthermore, Big Data approaches are increasingly needed for utilization of results from various Omics studies. Specific applications include predictive and content analytics that support drug discovery and optimization, the development of new diagnostic methods, and personalization of medicine. Biomedical data vary in granularity, quality, and complexity. There is a variety of sources and data formats - web pages, publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge.