Center for Predictive Computational Phenotyping (CPCP): Training Plans May 15, 2015 Debora Treu and Whitney Sweeney Center for Predictive Computational Phenotyping (CPCP) University of Wisconsin-Madison

What is CPCP? The Center for Predictive Computational Phenotyping (CPCP) promises to significantly advance the state of the art in computational methods for transforming large, heterogeneous, high-dimensional data sources into predictive models for biomedicine. Specifically, we are focusing on a broad range of problems that can be cast as computational phenotyping.

time now predict a clinically important phenotype before it is exhibited prospective phenotyping extract a relevant phenotype from a complex data source or collection of sources retrospective phenotyping ? The term phenotype refers to the observable properties of an organism that result from the interaction of its genotype and its environment. Some phenotypes are easily measured and interpreted, and are available in an accessible format. However, a wide range of scientifically and clinically important phenotypes do not satisfy these criteria. In such cases, computational phenotyping methods are required either to:

CPCP Aims Aim 1 Develop, evaluate and disseminate computational and statistical algorithms, models and software packages that significantly advance the state of the art in predictive computational phenotyping. Aim 2 Develop, conduct and evaluate training activities that reach a broad set of audiences whose education, research and practice would significantly benefit from having state-of-the-art knowledge about data science, predictive models for biomedicine, and computational phenotyping. These audiences include: Biomedical scientists Clinicians Data scientists Postdocs Graduate students Undergraduates General public

biomedical informatics computer science biostatistics engineering biological sciences bioethics & law medicine CPCP The Center for Predictive Computational Phenotyping

Types of Events Weekly Center Meetings Short courses Tutorial Modules “Boot camp” Tutorials Seminars Training in Undergraduate Research TED-style talks Annual Center Retreat

Summer Research Program in Computational Biology and Biostatistics (CBB) Nicholas Escanilla, Lake Forest College Mentor: David Page Vir Patel, Western Kentucky University Mentor: Sushmita Roy Emily Balczewski, Carleton College Mentor: Vikas Singh

Seminars on High-Throughput Computing Platforms The primary goal of these seminars are to help researchers make sense of what tools are available for the analysis of large and complex data sets and how they are relevant to their specific work. The Hadoop Ecosysytem: A Bird's-Eye Overview Jignesh Patel, CS This seminar addressed tools like Hadoop and Hive. It was videotaped and will be available on the CPCP website. CONDOR and the Center for High Throughput Computing Miron Livny, CS and Morgridge; Tim Cartwright, CS; Lauren Michael & Christina Koch, Morgridge This seminar will specifically deal with the tools provided by the Center for High Throughput Computing at the UW Madison. This seminar is tentatively scheduled for June It will also be videotaped and available on the CPCP website.

Small Talks on Big Data These “TED-style” talks have been created to introduce some of the scientific questions and quantitative problems with which the CPCP is grappling. Our goals are to share the knowledge and innovations derived while working on these issues as well as to promote collaborations with other data and biomedical scientists. Mark Craven, BMI Biomedical Big Data and the Computational Phenotyping Challenge Beth Burnside, Radiology Personalizing Breast Cancer: Integrating Predictive Phenotypes into Clinical Care David Page, BMI Predicting Health Events from Electronic Health Records by Machine Learning

The CPCP partners with the Morgridge Institute for Research and the Marshfield Research Clinic, and receives additional support from the UW-Madison School of Medicine and Public Health and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison with funding from the Wisconsin Alumni Research Foundation. This research is supported by the National Institutes of Health under Award Number U54AI The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.