Her team uses population-scale data with multiple layers of information (e.g. transcriptomic, DNA methylation, proteomic, brain imaging, clinical), and develops algorithms and software that incorporate prior knowledge of genotype-phenotype impact and systems-level organization for predictive modeling. Dr. Pai is also interested in the role of developmental epigenetics in altering the risk of childhood- and adult-onset disease.

Prior to joining OICR’s Computational Biology Program, Dr. Pai led the development of netDx – a general-purpose patient classifier algorithm integrates heterogenous patient data into a single model to predict clinical outcome. It adapts a recommender system model similar to that used by Netflix (“find movies like this one”) to precision medicine (“find patients like non-responders”), and uses prior knowledge of cellular pathways to organize genomic data. Separately, Dr. Pai co-led the first epigenome-wide association study in neurons isolated from post-mortem brain samples in major psychosis. This study led to the identification of a novel biomarker that unifies two distinctive features of psychosis: dopaminergic increase and reduced synaptic structure.

Dr. Pai’s work and expertise in genomics and precision medicine apply to cancer as well as other diseases with genomic contribution.