Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.

ABSTRACT: Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called

SUBMITTER: Praveen P 

PROVIDER: S-EPMC3691143 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature