Current Research Interests: Causal modeling of biomedical and clinical data; integration of multi-modal data using machine learning; clinical genomics; modeling transcriptional and post-transcriptional gene regulation; modeling of gene disease networks.
Identifying direct (causal) associations between biomedical or clinical variables is key to designing 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.
This WordCloud is based on titles and abstracts of papers published by members of the Benos’ group during 2018-2020.
Our lab is also part of the Genomics and Systems Biology Core of the Pittsburgh Liver Research Center.