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 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 m

SUBMITTER: Ni Y 

PROVIDER: S-EPMC5179539 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature