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dc.contributor.authorGrene, Ruthen_US
dc.contributor.authorHeath, Lenwood S.en_US
dc.contributor.authorLi, Songen_US
dc.contributor.authorCollakova, Evaen_US
dc.contributor.authorElmarekaby, Haithamen_US
dc.contributor.authorNi, Yingen_US
dc.contributor.authorAghamirzaie, Delasaen_US
dc.date.accessioned2016-12-26T19:06:29Z
dc.date.available2016-12-26T19:06:29Z
dc.date.issued2016-12-23en_US
dc.identifier.issn1664-462Xen_US
dc.identifier.urihttp://hdl.handle.net/10919/73833
dc.description.abstractGene 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 developing Arabidopsis thaliana embryos. 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 the cis elements 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.en
dc.languageEnglishen_US
dc.publisherFrontiers Mediaen_US
dc.relation.urihttp://home.frontiersin.org/en_US
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectGene Regulatory Networksen_US
dc.subjectSeed developmenten_US
dc.subjectRNA-Seq dataen_US
dc.subjectArabidopsisen_US
dc.titleA Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsisen_US
dc.typeArticle - Refereed
dc.description.versionPublished online (Publication status)en_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.departmentSchool of Plant and Environmental Sciencesen_US
dc.description.notesfalse (Extension publication?)en_US
dc.title.serialFrontiers in Plant Science Bioinformatics and Computational Biologyen_US
dc.identifier.doihttps://doi.org/10.3389/fpls.2016.01936
dc.identifier.issueDecember 23, 2016en_US
dcterms.dateAccepted2016-12-22en_US
pubs.organisational-group/Virginia Tech
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Faculty
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Plant Pathology, Physiology, & Weed Science
pubs.organisational-group/Virginia Tech/All T&R Faculty


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International