Dr. Bourne Keynote Address to CPCP Retreat 2016
Video of Dr. Phil Bourne, Associate Director of the NIH, giving keynote address at the CPCP Retreat.
Recent & Upcoming Events
Oct 18, 2016
Big Privacy: Policy Meets Data Science Symposium
This symposium examined the legal, policy, and technical issues at the intersection of data privacy
Apr 21, 2016
CPCP Seminar: Mining Structures from Massive Bio-Text Data: A Data-Driven Approach by Dr. Jiawei Han
Jiawei Han from the BD2K KnowEng Center-UIUC discussed mining structures from massive bio-text data
Nov 10, 2015
CPCP Seminar: Transforming Your Research with High-Throughput Computing by Lauren Michael
Lauren Michael from the CHTC discussed high-throughput computing approaches to Big Data
Oct 15, 2015
Big Privacy: Policy Meets Data Science Symposium
A symposium on the legal, policy, & technical issues at the intersection of privacy and data science
CPCP Retreat 2016: High-Throughput Computing in Support of High-Throughput Phenotyping Symposium Video
Dr. Miron Livny describes the opportunities available in terms of High-Throughput Computing at the UW Madison. In the past year, his team worked with close to 200 research teams, utilizing a total of 320 million computing hours. The High-Throughput Computing group facilitates data processing by "submitting locally and running globally" using many resources including the Open Science Grid (OSG). One of the most important resources the group has to offer is their team of expert consultants/liaisons who help scientists learn how to use High-Throughput Computing to effectively and efficiently accomplish their research goals.
CPCP Retreat 2016: High-Throughput Predictive Phenotyping from Electronic Health Records Symposium Video
Presented by Ross Kleiman
CPCP Retreat 2016: Using Active Learning to Phenotype Electronic Medical Records Symposium Video
Presented by Ari Biswas
CPCP Retreat 2016: Entity Matching for EHR- and Transcriptome-based Phenotyping Symposium Video
Presented by AnHai Doan
CPCP Retreat 2016: Computational Phenotyping for Breast Cancer Risk Assessment Symposium Video
Presented by Beth Burnside
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
EBSeqHMM is an R package that implements an auto-regressive hidden Markov model for identifying genes and isoforms that have expression changes in ordered RNA-seq experiments, and clustering the identified genes into paths showing similar changes. EBSeqHMM is suitable for any ordered RNA-seq experiment including time courses and spatially ordered experiments.
Oscope is a statistical pipeline for identifying oscillatory genes and characterizing one cycle of their oscillation, referred to as a base-cycle, in unsynchronized snapshot single cell RNA-seq experiments. The Oscope pipeline includes three modules: a paired-sine model module to identify candidate oscillator pairs; a clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to estimate the base-cycle oscillation within each group.
OEFinder is an R package that allows an investigator to identify genes having the so-called ordering effect in single-cell RNA-seq data generated by the Fluidigm C1 platform. This effect (Leng et al., Nature Methods, 2015) refers to significantly increased gene expression in cells captured from sites with small or large plate output IDs.
The role model is a probability model used in the context of gene set analysis to describe the functional content of a user-supplied gene list, such as one derived from a genome-wide experiment. It integrates the list with gene sets from a knowledge base (e.g. Gene Ontology) and aims to summarize gene functions that are represented at an unusually high rate on the list. Compared to other gene-set enrichment analysis schemes, role model calculations contend better with the complexity of the knowledge base, including redundancies caused by overlapping sets and the effects of set-size variation.
GADGET is a web tool that for finding and ranking genes and metabolites that are associated with a given query in the biomedical literature. It's like a version of PubMed that returns genes and metabolites instead of articles.