Computational Biology

The OHSU computational biology program supports the development of innovative computational approaches to a range of programs in basic and translational research throughout OHSU. Research in the computational biology program focuses on integrative analysis of high dimensional heterogeneous molecular, imaging, and clinical data to infer predictive models of disease-related phenotypes and functional interventions that induce desired phenotypic transitions. The computational biology team has advanced training in machine learning, statistical techniques, and a track record of innovation in a biomedical application, and are ready to provide any support that might be necessary for the OHSU research and clinical community.

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OHSU will lead the next generation of genomically guided therapies and early detection by discovering novel data-driven genotype/phenotype association inferred from large-scale analytics, and translating them to benefit clinical care. To enable this vision, the computational biology program will be guided by the following priorities:

  • Demonstrate benefit to patients through advances in big data analytics
  • Enable scientific goals of cross-departmental programs/projects
  • Establish OHSU as a world leader in computational biology research


In order to be an effective team we have implemented the following strategic goals:

  • Move OHSU into the big data era through organizational structures, systems, and values fostering goal-directed team science
  • Engage researchers to continuously build on work of colleagues around priority projects with high value-add across multiple basic science projects
  • Initial build focus on integrative data analysis (including omics and imaging data) to predict disease phenotypes from clinical and genetic information
  • Future build focus on early detection
  • Develop strategic partnerships with world-leading efforts in priority areas


The overall objectives for the team are as follows:

  • Align existing efforts through standardization of tools and systems
  • Build expertise in advanced probabilistic inference methods for integrative data analysis
  • Build expertise in high-level, team oriented software development
  • Create a transparent, accessible body of data inter-operable with large-scale public data sources
  • Advance research goals across multiple basic science initiatives
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