Computational medicine: Translating models to clinical care

Raimond L. Winslow, Natalia Trayanova, Donald Geman, Michael I. Miller

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Article number158rv11
JournalScience translational medicine
Volume4
Issue number158
DOIs
StatePublished - Oct 31 2012

ASJC Scopus subject areas

  • Medicine(all)

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