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)
Program Officer
Mathur, Punam
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Wisconsin Madison
Biostatistics & Other Math Sci
Schools of Medicine
United States
Zip Code
Geng, Sinong; Kuang, Zhaobin; Liu, Jie et al. (2018) Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error. Uncertain Artif Intell 2018:156-166
Zhou, Hao Henry; Singh, Vikas; Johnson, Sterling C et al. (2018) Statistical tests and identifiability conditions for pooling and analyzing multisite datasets. Proc Natl Acad Sci U S A 115:1481-1486
Bacher, Rhonda; Leng, Ning; Chu, Li-Fang et al. (2018) Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinformatics 19:380
Ong, Irene M; Gonzalez, Jose G; McIlwain, Sean J et al. (2018) Gut microbiome populations are associated with structure-specific changes in white matter architecture. Transl Psychiatry 8:6
Harrison, Christopher; Kele?, Sündüz; Hudson, Rebecca et al. (2018) atSNPInfrastructure, a case study for searching billions of records while providing significant cost savings over cloud providers. IEEE Int Symp Parallel Distrib Process Workshops Phd Forum 2018:497-506
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

Showing the most recent 10 out of 103 publications