Network inference from diverse genomics data: Interactions among genes and their gene products comprise a regulatory network. The goal of network inference is to generate testable hypotheses of gene-to-gene influences and subsequently design bench experiments to confirm network predictions. In the November 2011 issue of PNAS, Yeung and colleagues presented a methodology to construct gene regulatory networks from time series expression data in yeast, integrating various types of external biological knowledge available from public repositories. We generated microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. Our algorithm is capable of generating feedback loops and we showed that the inferred network recovered existing and novel regulatory relationships. In addition, we generated independent microarray data on selected deletion mutants to prospectively test network predictions. Related work: BMC Systems Biology 2012, BMC Systems Biology 2014
From computational discoveries to translational research: The development of genetic predictors of clinical outcomes contributes to risk assessment in personalized medicine. In collaboration with Dr. Jerry Radich and Dr. Vivian Oehler at the Fred Hutchinson Cancer Research Center, we aim to develop computational models that can predict patient responses to therapy at diagnosis, which allow us to tailor therapy to individual patients of chonic myeloid leukemia (CML). We have previously applied Bayesian Model Averaging to a gene expression data studying the progression of CML, and identified 6 predictive genes in Blood 2009. Building on this work, we developed a network-driven approach that uses expert knowledge and predicted functional relationships to guide our search for signature genes in the March 2012 issue of Bioinformatics. We showed that our gene signatures of advanced phase CML are predictive of relapse even after adjustment for known risk factors associated with transplant outcomes.