Inferring Host-Pathogen Interactions from Diverse Data Sources
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Dr. Craven discusses work in several studies that involve developing and applying predictive methods in order to characterize host-pathogen interactions. In the first study, we are focused on inferring host subnetworks that are involved in viral replication from genome-wide loss-of-function experiments. Although these experiments can identify the host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell, they do not elucidate how these genes are organized into the biological pathways that mediate host-virus interactions. We are developing novel computational methods that use a wide array of secondary data sources, including the scientific literature, to transform the measurements from these assays into hypotheses that predict the pathways in the cell that relate implicated genes to viral replication. In the second study, we are applying machine-learning methods to understand how variation in the genome of the HSV-1 virus influences multiple ocular disease phenotypes in a host. In the third study, we are investigating the extent to which risk for various infectious disease phenotypes can be predicted from electronic health records by using machine-learning methods.