EHR-based Phenotyping Project
Neuroimage-based Phenotyping Project
Epigenome-based Phenotyping Project
Transcriptome-based Phenotyping Project
Phenotype Models for Breast Cancer Screening Project
Stochastic Modeling Lab
Low-dimensional Representations Lab
Data Management Lab
Value of Information Lab
Software Engineering and High-Throughput Computing Core
Bioethics Core
Recent CPCP Publications
Understanding learned models by identifying important features at the right resolution. Lee K, Sood A, Craven M. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019
Combined hypothesis testing on graphs with applications to gene set enrichment analysis. Wang S, Yuan M. Journal of the American Statistical Association, 2018
Practical model selection for prospective virtual screening. Liu S, Alnammi M, Ericksen S, Voter A, Ananiev G, Keck J, Hoffmann FM, Wildman S, Gitter A. Journal of Chemical Information and Modeling, 2018
Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. Feld S, Woo K, Alexandridis R, Wu Y, Liu J, Peissig P, Onitilo A, Cox J, Page D, Burnside E. Proceedings of the AMIA Annual Symposium, 2018
Privacy-preserving ridge regression with only linearly-homomorphic encryption. Giacomelli I , Jha S, Joye M, Page CD, Yoon K. Proceedings of Applied Cryptography & Network Security (ACNS), 2018
Opportunities and obstacles for deep learning in biology and medicine. Ching T et al. Journal of the Royal Society Interface 15:20170387, 2018
MatchCatcher: A debugger for blocking in entity matching. Li H, Konda P, Suganthan P, Doan A, Snyder B, Park Y, Krishnan G, Deep R, Raghavendra V. Proceedings of International Conference on Extending Database Technology (EDBT), 2018
Utility of genetic testing in addition to mammography for determining risk of breast cancer depends on patient age. Feld S, Fan J, Yuan M, Wu Y, Woo K, Alexandridis R, Burnside E. Proceedings of the AMIA Informatics Summit, 2018
Using machine learning algorithms to predict risk for development of calciphylaxis in patients with chronic kidney disease. Kleiman R, LaRose E, Badger J, Page D, Caldwell M, Clay J, Peissig P. Proceedings of the AMIA Informatics Summit, 2018