Current Research Interests: Causal modeling of biomedical data; integration of multi-modal data; clinical genomics; modeling transcriptional and post-transcriptional gene regulation; modeling of gene disease networks.
Identifying direct (causal) associations between biomedical variables is key to developing Precision Medicine strategies. The Benos group develops machine learning methods to integrate heterogeneous data into a single probabilistic graphical framework. The resulting graphs can then be used to infer causal interactions, uncover disease mechanisms, select biomarkers or help stratify patient populations.
The Benos group is also interested in understanding gene regulation, its dynamics, the mechanistic characteristics of the protein-DNA and microRNA-mRNA interactions and the characteristics of their “signals” in the context of disease gene networks.