Integration of Multiple Data Sources for Gene Network Inference Using Genetic Perturbation Data.

ABSTRACT: The inference of gene networks from large-scale human genomic data is challenging due to the difficulty in identifying correct regulators for each gene in a high-dimensional search space. We present a Bayesian approach integrating external data sources with knockdown data from human cell lines to infer gene regulatory networks. In particular, we assemble multiple data sources, including gene expression data, genome-wide binding data, gene ontology, and known pathways, and use a supervised learning framework to compute prior probabilities of regulatory relationships. We show that our integrated method improves the accuracy of inferred gene networks as well as extends some previous Bayesian frameworks both in theory and applications. We apply our method to two different human cell lines,

SUBMITTER: Liang X 

PROVIDER: S-EPMC6786343 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature