|
Abstract:
|
The functional cancer genomic and proteomic data provide rich sources of information to identify variations in signaling pathways and activities within and across tumor lineages. I discuss the following new tools: (1) DINGO : a pathway-based differential network analysis in genomics model for estimating group-specific networks and making inference on the differential networks. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development. (2) PRECISE - a general framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures, which are subsequently used for tumor subtype classification and clinical outcome prediction. (3) bHUB: a Bayesian approach to the problem of multiple network inference which allows direct inference on shared and differential hub nodes, utilizing information borrowing regarding hub nodes across networks. We illustrate the methods using TCGA cancer data.
|
