High-throughput computing and machine learning boost drug screening
New approach shows value of combining the results of multiple docking programs
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
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.
CPCP Seminar: Friends Don't Let Friends Use Black-Box Models: The Importance of Intelligibility in Machine Learning for Healthcare Seminar Video
Talk by Rich Caruana PhD, Microsoft Research
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
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
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
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 is an R package to identify genes with distributional changes across conditions in a single-cell RNA-seq experiment
scPattern is an R package to identify and classify gene expression changes in ordered single-cell RNA-seq experiments
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.