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 Home

News

• 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

Contact Maria

phone: 412 648 3338
email: mchikina@pitt.edu