The Center for Predictive Computational Phenotyping is focused on significantly advancing the state of the art in computational methods for transforming large, heterogeneous, high-dimensional data sources into predictive models for biomedicine. Specifically, we are focusing on a broad range of problems that can be cast as computational phenotyping. CPCP is organized into projects which are focused on model problems for computational phenotyping, labs which are developing innovative methodological approaches, and two key cores.

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

Anxiety-related experience-dependent white matter structural differences in adolescence: A monozygotic twin difference approach. Adluru N, Luo Z, VanHulle CA, Schoen AJ, Davidso, RJ, Alexander AL, Goldsmith HH. Scientific Reports, 7(1): 8749, 2017

When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, L2-consistency and Neuroscience Applications. Zhou HH, Zhang Y, Ithapu VK, Johnson SC, Wahba G, Singh V. Proceedings of the International Conference on Machine Learning (ICML), 2017

Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging. Kim HJ, Adluru N, Suri H, Vemuri BC, Johnson SC, Singh V. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

Machine learning consensus scoring improves performance across targets in structure-based virtual screening. Ericksen S, Wu H, Zhang H, Michael L, Newton M, Hoffmann FM, Wildman S. Journal of Chemical Information and Modeling 57(7):1579–1590, 2017

Pharmacovigilance via baseline regularization with large-scale longitudinal observational data. Kuang Z, Peissig P, Santos Costa V, Maclin R, Page D. Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017

A review of active learning approaches to experimental design for uncovering biological networks. Sverchkov Y, Craven M. PLoS Computational Biology, 2017

MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive. Bernstein M, Doan A, Dewey C. Bioinformatics, 2017

Falcon: Scaling up hands-off crowdsourced entity matching to build cloud services. Das S, Suganthan P, Doan A, Naughton J, Krishnan G, Deep R, Arcaute E, Raghavendra V, Park Y. Proceedings of the ACM International Conference on Management of Data (SIGMOD), 2017

Chromatin module inference on cellular trajectories identifies key transition points and poised epigenetic states in diverse developmental processes. Roy S, Sridharan R. Genome Research, 2017

SCnorm: robust normalization of single-cell RNA-seq data. Bacher R, Chu LF, Leng N, Gasch A, Thomson J, Stewart R, Newton M, Kendziorski C. Nature Methods, 2017