A prior-based integrative framework for functional transcriptional regulatory network inference.

ABSTRACT: Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze o

SUBMITTER: Siahpirani AF 

PROVIDER: S-EPMC5389674 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature