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.
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
- New-preprint: "Prediction of local convergent shifts in evolutionary rates with phyloConverge characterizes the phenotypic associations and modularity of regulatory elements" bioRxiv
- “Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data is now online at Bioinformatics
- New preprint: Widespread redundancy in -omics profiles of cancer mutation states. In corllaboration with Casey Geene's group 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