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Title: Enhancing gene regulatory network inference through data integration with markov random fields

Here, 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.
 [1] ;  [1]
  1. Carnegie Institution for Science, Stanford, CA (United States)
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2045-2322
Nature Publishing Group
Research Org:
Donald Danforth Plant Science Center, St. Louis, MO (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; gene regulatory networks; machine learning
OSTI Identifier: