title: A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis.
abstract: Gene regulatory networks (GRNs) provide a representation of relationships between regulators and their target genes. Several methods for GRN inference, both unsupervised and supervised, have been developed to date. Because regulatory relationships consistently reprogram in diverse tissues or under different conditions, GRNs inferred without specific biological contexts are of limited applicability. In this report, a machine learning approach is presented to predict GRNs specific to developingArabidopsis thalianaembryos. We developed the Beacon GRN inference tool to predict GRNs occurring during seed development in Arabidopsis based on a support vector machine (SVM) model. We developed both global and local inference models and compared their performance, demonstrating that local models are generally superior for our application. Using both the expression levels of the genes expressed in developing embryos and prior known regulatory relationships, GRNs were predicted for specific embryonic developmental stages. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. Potential direct targets were identified based on a match between the promoter regions of these inferred targets and theciselements recognized by specific regulators. Our analysis also provides evidence for previously unknown inhibitory effects of three positive regulators of gene expression. The Beacon GRN inference tool provides a valuable model system for context-specific GRN inference and is freely available at https://github.com/BeaconProjectAtVirginiaTech/beacon_network_inference.git.
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ni
authors.affiliation: Genetics, Bioinformatics and Computational Biology, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
authors.forename: Delasa
authors.initials: D
authors.lastname: Aghamirzaie
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
authors.forename: Haitham
authors.initials: H
authors.lastname: Elmarakeby
authors.affiliation: Department of Plant Pathology, Physiology, and Weed Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
authors.forename: Eva
authors.initials: E
authors.lastname: Collakova
authors.affiliation: Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
authors.forename: Song
authors.initials: S
authors.lastname: Li
authors.affiliation: Department of Plant Pathology, Physiology, and Weed Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
authors.forename: Ruth
authors.initials: R
authors.lastname: Grene
authors.affiliation: Department of Computer Science, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
authors.forename: Lenwood S
authors.initials: LS
authors.lastname: Heath
keywords: Arabidopsis; binding site; cluster; gene expression; gene regulatory network; support vector machines