A powerful knowledge engine for genomics data analysis
I will describe current progress of the KnowEnG Center, emphasizing the novel algorithms that we have developed and applied to the discovery of mechanisms underlying diverse phenotypes such as drug response and social behavior. For instance, we have developed:
(1) a technique based on diffusion component analysis that identifies cancer pathways associated with drug response,
(2) an approach that uses network-smoothing of gene expression data and random walks with restart on the Knowledge Network to better rank cytotoxicity-related genes,
(3) a probabilistic graphical model that integrates genotype, gene expression and transcription factor-DNA binding data with drug response data to identify regulatory mechanisms of drug response variation across individuals, and
(4) random walk-based methods for gene set characterization, as an alternative to existing techniques such gene set enrichment analysis, using it to glean systems-level insights about social regulation of aggressive behavior.
I will present key ideas of these new approaches to knowledge-guided analysis of omic data sets, as well as major features of the Cloud-based knowledge engine enabling these analyses.