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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54AI117924-01
Application #
8774800
Study Section
Special Emphasis Panel ()
Program Officer
Dugan, Vivien G
Project Start
2014-09-29
Project End
2018-08-31
Budget Start
2014-09-29
Budget End
2015-04-30
Support Year
1
Fiscal Year
2014
Total Cost
$1,991,007
Indirect Cost
$635,898
Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Bozkurt, Selen; Gimenez, Francisco; Burnside, Elizabeth S et al. (2016) Using automatically extracted information from mammography reports for decision-support. J Biomed Inform 62:224-31
Li, Hui; Zhu, Yitan; Burnside, Elizabeth S et al. (2016) MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology 281:382-391
Liu, Peng; Sanalkumar, Rajendran; Bresnick, Emery H et al. (2016) Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq. Genome Res 26:1124-33
Chu, Li-Fang; Leng, Ning; Zhang, Jue et al. (2016) Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol 17:173
Sievers, Chelsie K; Zou, Luli S; Pickhardt, Perry J et al. (2016) Subclonal diversity arises early even in small colorectal tumours and contributes to differential growth fates. Gut :
Burnside, Elizabeth S; Liu, Jie; Wu, Yirong et al. (2016) Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Acad Radiol 23:62-9
Hwang, Seong Jae; Adluru, Nagesh; Collins, Maxwell D et al. (2016) Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016:2517-2525
Gasch, Audrey P; Hose, James; Newton, Michael A et al. (2016) Further support for aneuploidy tolerance in wild yeast and effects of dosage compensation on gene copy-number evolution. Elife 5:e14409
Bacher, Rhonda; Kendziorski, Christina (2016) Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol 17:63
Kim, Won Hwa; Hwang, Seong Jae; Adluru, Nagesh et al. (2016) Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging. Comput Vis ECCV 9910:188-205

Showing the most recent 10 out of 46 publications