Bayesian variable selection with graphical structure learning: Applications in integrative genomics.

ABSTRACT: Significant advances in biotechnology have allowed for simultaneous measurement of molecular data across multiple genomic, epigenomic and transcriptomic levels from a single tumor/patient sample. This has motivated systematic data-driven approaches to integrate multi-dimensional structured datasets, since cancer development and progression is driven by numerous co-ordinated molecular alterations and the interactions between them. We propose a novel multi-scale Bayesian approach that combines integrative graphical structure learning from multiple sources of data with a variable selection framework-to determine the key genomic drivers of cancer progression. The integrative structure learning is first accomplished through novel joint graphical models for heterogeneous (mixed scale) data, allo

SUBMITTER: Kundu S 

PROVIDER: S-EPMC6066211 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature