title: Computational medicine: translating models to clinical care.
abstract: Because of the inherent complexity of coupled nonlinear biological systems, the development of computational models is necessary for achieving a quantitative understanding of their structure and function in health and disease. Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. Multiscale modeling links networks to cells, organs, and organ systems. Computational approaches are used to characterize anatomic shape and its variations in health and disease. In each case, the purposes of modeling are to capture all that we know about disease and to develop improved therapies tailored to the needs of individuals. We discuss advances in computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Advances in translating these computational methods to the clinic are described, as well as challenges in applying models for improving patient health.
authors.affiliation: The Institute for Computational Medicine, Center for Cardiovascular Bioinformatics and Modeling, and Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD 21218, USA. rwinslow@jhu.edu
authors.forename: Raimond L
authors.initials: RL
authors.lastname: Winslow
authors.forename: Natalia
authors.initials: N
authors.lastname: Trayanova
authors.forename: Donald
authors.initials: D
authors.lastname: Geman
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008511:Medicine / Q000379:methods*; D008962:Models, Theoretical