title: Enhancing gene regulatory network inference through data integration with markov random fields.
abstract: A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE's potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.
authors.affiliation: Department of Plant Biology, Carnegie Institution for Science, 93405 Stanford, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Banf
authors.affiliation: Department of Plant Biology, Carnegie Institution for Science, 93405 Stanford, USA.
authors.forename: Seung Y
authors.initials: SY
authors.lastname: Rhee
mesh_terms: D000465:Algorithms; D000818:Animals; D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000235:genetics / Q000378:metabolism; D049109:Cell Proliferation; D019295:Computational Biology / Q000379:methods*; D029721:Drosophila Proteins / Q000235:genetics / Q000378:metabolism; D004331:Drosophila melanogaster / Q000235:genetics* / Q000378:metabolism; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008390:Markov Chains; D008957:Models, Genetic; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D029681:Arabidopsis Proteins; D029721:Drosophila Proteins; D014157:Transcription Factors