CPCP Second Annual Retreat
Jun 30, 2016
8:00 am - 5:00 pm
Wisconsin Institutes for Discovery 330 N. Orchard St. Madison, WI
The day-long program will feature a keynote lecture by Dr. Phil Bourne, the Associate Director for Data Science of the NIH, and presentations by CPCP faculty, postdocs and students. In addition to presentations, there will be poster sessions, and ample opportunities to connect with the speakers and other event attendees.
The topics covered in CPCP presentations will include:
- Modeling and predicting thousands of phenotypes from electronic health records and RNAseq profiles;
- genome-wide characterization of polymorphisms and regulatory elements;
- large-scale entity matching for experiment metadata and drug descriptors;
- innovative new approaches for inferences on graph partitions, learning predictive models for manifold-valued responses, and penalized learning methods that leverage structured information;
- applications to breast-cancer screening and Alzheimer's disease.
The Center for Predictive Computational Phenotyping is developing innovative computational and statistical methods and software for a broad range of problems that involve extracting relevant phenotypes from complex data sources and predicting clinically important phenotypes before they are exhibited. The Center is investigating how to exploit a wide array of data types for these tasks, including molecular profiles, medical images, electronic health records, and population-level data. To see the complete agenda for the day, please click here.
Related Resources
CPCP Retreat 2016: Neuroimage-Based Phenotyping and the Problem of AD
CPCP Retreat 2016: Multi-Armed Bandit Algorithms and Applications to Experiment Selection
CPCP Retreat 2016: Computational Phenotyping for Breast Cancer Risk Assessment
CPCP Retreat 2016: Entity Matching for EHR- and Transcriptome-based Phenotyping
CPCP Retreat 2016: Using Active Learning to Phenotype Electronic Medical Records
CPCP Retreat 2016: High-Throughput Predictive Phenotyping from Electronic Health Records
CPCP Retreat 2016: High-Throughput Computing in Support of High-Throughput Phenotyping