Using large-scale genomic datasets we develop new machine learning models that facilitate discovery and interpretation. We work on a variety of biological problems ranging from multi-omics analysis to molecular evolution.
We work with genomics data consortia
We are part of the Epigenetic CHaracterization and Observation (ECHO) . The program is building a man-portable device that analyzes an individual’s epigenetic “fingerprint” to potentially reveal a detailed history of that individual’s exposure to WMD or their precursors.
Our group is part of the Molecular Transducers of Physical Activity Consortium (MoTraPAC) . This is a large study looking at the effects of exercies through multiple genomic assays.
Our collaborations leverage our strong biomedical community
Current collaborators include:
Nathan Clark Group, Department of Computational and Systems Biology
Current project: RERconverge method for connecting evolutionary forces with phenotypes.
Mark Shlomchik Group, Department of Immunology
Current project: Understanding memory B-cell subtypes through multiomics analysis.
Hassane Zarour's Group, UPMC Hillman Cancer Institute
Current project: Translational tumor immunology
Paul Monga's Group, Department of Pathology
Current project: Liver disease models.
University of Pittsburgh School of Medicine
Department of Computational and Systems Biology
3501 Fifth Ave, Pittsburgh, PA 15260
Carnegie Mellon - University of Pittsburgh
Ph.D. Program in Computational Biology
- You can now read our new preprint about controlling type-I error in phylogenetic analyses. "Phylogenetic Permulations: a statistically rigorous approach to measure confidence in associations between phenotypes and genetic elements in a phylogenetic context" bioRxiv
- “DataRemix: a universal data transformation for optimal inference from gene expression datasets” is now online at Bioinformatics
- “Causal network perturbations for instance-specific analysis of single cell and disease samples” with Kristina Buschur and Takis Benos is now online at Bioinformatics
- PLIER is used to infer gene-enironment eQTL interactions. Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain
- Our method PLIER is online at Nature Methods. Nature Methods
- Wynn's paper is online at Science With write-ups in NYtimes and National Geographic
phone: 412 648 3338