Research

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

Structure-leveraged methods in breast cancer risk prediction. Fan J, Wu Y, Yuan M, Page D, Liu J, Ong IM, Peissig P, Burnside E. Journal of Machine Learning Research 17:1-15, 2016

Modeling the temporal evolution of postoperative complications. Feld SI, Cobian AG, Tevis SE, Kennedy GD, Craven MW. Proceedings of the American Medical Informatics Association Annual Symposium, 2016

Adaptive signal recovery on graphs via harmonic analysis for experimental design in neuroimaging. Kim WH, Hwang SJ, Adluru N, Johnson SC, Singh V. Proceedings of the 14th European Conference on Computer Vision (ECCV), Volume 9910 Lecture Notes in Computer Science, 2016

Magellan: toward building entity matching management systems. Konda P, Das S, Suganthan P, Doan A, Ardalan A, Ballard JR, Li H, Panahi F, Zhang H, Naughton J, Prasad S, Krishnan G, Deep R, Raghavendra V. Proceedings of the 42nd International Conference on Very Large Databases, 2016

Baseline regularization for computational drug repositioning with longitudinal observational data. Kuang Z, Thomson J, Caldwell M, Peissig P, Stewart R, Page D. Proceedings of the 25th International Joint Conference on Artificial Intelligence, 2016

Computational drug repositioning using continuous self-controlled case series. Kuang Z, Thomson J, Caldwell M, Peissig P, Stewart R, Page D. Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016

Latent variable graphical model selection using harmonic analysis: applications to the Human Connectome Project (HCP). Kim WH, Kim HJ, Adluru N, Singh V. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

Experimental design on a budget for sparse linear models and applications. Ravi SN, Ithapu VK, Johnson SC, Singh V. Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016

Coupled harmonic bases for longitudinal characterization of brain networks. Hwang SJ, Adluru N, Collins MD, Ravi SN, Bendlin BB, Johnson SC, Singh V. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

A hierarchical framework for state space matrix inference and clustering. Zuo C, Chen K, Hewitt K, Bresnick EH, Keles S. Annals of Applied Statistics, 2016