Recent & Upcoming Events

Oct 31, 2017

The Fairsquare Project: Countering Programs that Discriminate by Dr. Aws Albarghouthi

CPCP Privacy/Fairness Seminar

Oct 24, 2017

Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning for Healthcare by Dr. Rich Caruana

CPCP Privacy/Fairness Seminar

Sep 26, 2017

The Bounty of the Commons by Dr. Casey Greene

CPCP Privacy/Fairness Seminar

Jun 1, 2017

CPCP Third Annual Retreat

The day-long program will feature presentations about using Big Data to improve human health.

Apr 1, 2017

CPCP at UW-Madison Science Expeditions

Come explore how the CPCP is using Big Data to improve human health.

Training Resources

CPCP Seminar: The Bounty of the Commons Seminar Video

Casey Greene, PhD University of Pennsylvania Abstract: This is an exciting time in biomedical data science. It is now possible to collect substantial information about individuals and their encounters with health care. Our ultimate goal is to integrate this data, along with data and findings from those engaged in basic science, to identify new opportunities to improve health. Broad data sharing will further our progress towards this goal. However, data sharing poses both cultural and technological challenges. I'll discuss our work to address technical issues, including analysis approaches that lift techniques from the field of software engineering and data sharing approaches that employ deep generative neural networks. I'll also touch on our work to shift cultures, including the research parasite and research symbiont awards (applications for each due Sept 30!).

CPCP 2017 Retreat: Phenotype Models for Breast Cancer Screening Symposium Video

Talk by Beth Burnside and Ming Yuan

CPCP 2017 Retreat: Entity Matching Using Magellan - Matching Drug Reference Tables Symposium Video

Talk by Peggy Peissig

CPCP 2017 Retreat: Improved Methods for Discovering Adverse Drug Events from EHR Data Symposium Video

Talk by David Page

CPCP 2017 Retreat: Privacy-Preserving Machine Learning Symposium Video

Talk by Irene Giacomelli

Recent 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

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

Recent Resources

CMINT software

Chromatin Module INference on Trees (CMINT) is an algorithm for learning chromatin modules, defined as groups of genomic loci that have similar chromatin states. Chromatin states in turn are defined by a combination of chromatin mark profiles.

scDD software

scDD is an R package to identify genes with distributional changes across conditions in a single-cell RNA-seq experiment

scPattern software

scPattern is an R package to identify and classify gene expression changes in ordered single-cell RNA-seq experiments

RIPPLE software

Regulatory interaction prediction for promoters and long-range enhancers

MetaSRA: normalized metadata for the Sequence Read Archive data

MetaSRA is an annotation/re-coding of sample-specific metadata in the Sequence Read Archive using biomedical ontologies. Currently, MetaSRA maps biological samples to biologically relevent terms in the Disease Ontology, Experimental Factor Ontology, Cell Ontology, Uberon, and Cellosaurus.