PiMGM: incorporating multi-source priors in mixed graphical models for learning disease networks.

ABSTRACT:

Motivation

Learning probabilistic graphs over mixed data is an important way to combine gene expression and clinical disease data. Leveraging the existing, yet imperfect, information in pathway databases for mixed graphical model (MGM) learning is an understudied problem with tremendous potential applications in systems medicine, the problems of which often involve high-dimensional data.

Results

We present a new method, piMGM, which can learn with accuracy the structure of probabilistic graphs over mixed data by appropriately incorporating priors from multiple experts with different degrees of reliability. We show that piMGM accurately scores the reliability of prior information from a given expert even at low sample sizes. The reliability scores can be used to determine act

SUBMITTER: Manatakis DV 

PROVIDER: S-EPMC6129280 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature