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
Giovanni, Maria Y
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
Strigel, Roberta M; Burnside, Elizabeth S; Elezaby, Mai et al. (2017) Utility of BI-RADS Assessment Category 4 Subdivisions for Screening Breast MRI. AJR Am J Roentgenol 208:1392-1399
Fan, Jun; Yuan, Ming (2017) Comments on ""Personalized dose finding using outcome weighted learning"". J Am Stat Assoc 111:1524-1525
P Tafti, Ahmad; Badger, Jonathan; LaRose, Eric et al. (2017) Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR Med Inform 5:e51
Adluru, Nagesh; Luo, Zhan; Van Hulle, Carol A et al. (2017) Anxiety-related experience-dependent white matter structural differences in adolescence: A monozygotic twin difference approach. Sci Rep 7:8749
Bacher, Rhonda; Chu, Li-Fang; Leng, Ning et al. (2017) SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods 14:584-586
Racine, Annie M; Merluzzi, Andrew P; Adluru, Nagesh et al. (2017) Association of longitudinal white matter degeneration and cerebrospinal fluid biomarkers of neurodegeneration, inflammation and Alzheimer's disease in late-middle-aged adults. Brain Imaging Behav :
Strigel, Roberta M; Rollenhagen, Jennifer; Burnside, Elizabeth S et al. (2017) Screening Breast MRI Outcomes in Routine Clinical Practice: Comparison to BI-RADS Benchmarks. Acad Radiol 24:411-417
Shin, Sunyoung; Kele?, Sündüz (2017) Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data. Stat Biosci 9:50-72
Choi, J; Ye, S; Eng, K H et al. (2017) IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients. Stat Biosci 9:1-12
Ericksen, Spencer S; Wu, Haozhen; Zhang, Huikun et al. (2017) Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening. J Chem Inf Model 57:1579-1590

Showing the most recent 10 out of 70 publications