Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information.

ABSTRACT: Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for

SUBMITTER: Kim S 

PROVIDER: S-EPMC5405575 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature