Identifying novel biological insights on complex human diseases requires spanning both the biological and computational worlds. Our biological data mining strategies aim to do this by helping to ask the right computational questions in order to get the right biological answers.

One major part of our group’s research is to facilitate generation of large scale datasets which can be integrated into causal predictive molecular networks which we then interpret for the pathophysiology of underlying complex biological questions. Our network insights are refined into candidate genes and pathways and formulated into testable hypotheses, some of which we perform experimental validation of ourselves.

Ultimately we aim to use this knowledge to predict novel therapeutic candidates for diseases of interest. We have applied these methods in multiple disciplines covering complex diseases (metabolic syndrome, IBD and cancer), complex traits (aging) as well as inborn errors of metabolism (Gaucher’s disease and Mitochondrial disorders).

Our system biology approaches are highly collaborative projects as they require various expertise from clinicians in the clinic, to experimentalist in the wet lab to the computational analyst in the dry lab. Three of my group’s main projects are summarized:

Icahn Institute for Data Science and Genomic Technology Faculty

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