Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity.
Journal:
BMC systems biology
Published Date:
Feb 11, 2016
Abstract
BACKGROUND: High throughput technologies have been used to profile genes in multiple different dimensions, such as genetic variation, copy number, gene and protein expression, epigenetics, metabolomics. Computational analyses often treat these different data types as independent, leading to an explosion in the number of features making studies under-powered and more importantly do not provide a comprehensive view of the gene's state. We sought to infer gene activity by integrating different dimensions using biological knowledge of oncogenes and tumor suppressors.