The biomedical sciences are being radically transformed by advances in our ability to monitor, record, store and integrate information characterizing human biology and health at scales that range from individual molecules to large populations of subjects. This wealth of information has the potential to substantially advance both our understanding of human biology and our ability to improve human health. Perhaps the most central and general approach for exploiting biomedical data is to use methods from machine learning and statistical modeling to infer predictive models. Such models take as input observable data representing some object of interest, and produce as output a prediction about a particular, unobservable property of the object. This approach has proven to be of high value for a wide range of biomedical tasks, but numerous significant challenges remain to be solved in order for the full potential of predictive modeling to be realized. To address these challenges, we propose to establish The Center for Predictive Computational Phenotyping (CPCP). Our proposed center will focus on a broad range of problems that can be cast as computational phenotyping. Although some phenotypes are easily measured and interpreted, and are available in an accessible format, a wide range of scientifically and clinically important phenotypes do not satisfy these criteria. In such cases, computational phenotyping methods are required either to (i) extract a relevant phenotype from a complex data source or collection of heterogeneous data sources, (ii) predict clinically important phenotypes before they are exhibited, or (iii) do both in the same application.

Public Health Relevance

We will develop innovative new approaches and tools that are able to discover, and make crucial inferences with large data sets that include molecular profiles, medical images, electronic health records, population level data, and various combinations of these and other data types. These approaches will significantly advance the state of the art in wide range of biological and clinical investigations, such as predicting which patients are most at risk for breast cancer, heart attacks and severe blood clots.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BST-Z (52))
Program Officer
Dugan, Vivien G
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Wisconsin Madison
United States
Zip Code
Keller, Mark P; Gatti, Daniel M; Schueler, Kathryn L et al. (2018) Genetic Drivers of Pancreatic Islet Function. Genetics 209:335-356
Shulei Wang; Arena, Ellen T; Eliceiri, Kevin W et al. (2018) Automated and Robust Quantification of Colocalization in Dual-Color Fluorescence Microscopy: A Nonparametric Statistical Approach. IEEE Trans Image Process 27:622-636
Huang, Xiayuan; Elston, Robert C; Rosa, Guilherme J et al. (2018) Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics 34:635-642
Köksal, Ali Sinan; Beck, Kirsten; Cronin, Dylan R et al. (2018) Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data. Cell Rep 24:3607-3618
Pleiman, Jennifer K; Irving, Amy A; Wang, Zhishi et al. (2018) The conserved protective cyclic AMP-phosphodiesterase function PDE4B is expressed in the adenoma and adjacent normal colonic epithelium of mammals and silenced in colorectal cancer. PLoS Genet 14:e1007611
Zhang, Qi; Keles, Sündüz (2018) An empirical Bayes test for allelic-imbalance detection in ChIP-seq. Biostatistics 19:546-561
Zhang, Yi; Manjunath, Mohith; Zhang, Shilu et al. (2018) Integrative Genomic Analysis Predicts Causative Cis-Regulatory Mechanisms of the Breast Cancer-Associated Genetic Variant rs4415084. Cancer Res 78:1579-1591
Feld, Shara I; Fan, Jun; Yuan, Ming et al. (2018) Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age. AMIA Jt Summits Transl Sci Proc 2017:81-90
Wu, Yirong; Fan, Jun; Peissig, Peggy et al. (2018) Quantifying predictive capability of electronic health records for the most harmful breast cancer. Proc SPIE Int Soc Opt Eng 10577:
Reinhart, Jennifer M; Rose, Warren; Panyard, Daniel J et al. (2018) RNA expression profiling in sulfamethoxazole-treated patients with a range of in vitro lymphocyte cytotoxicity phenotypes. Pharmacol Res Perspect 6:e00388

Showing the most recent 10 out of 103 publications