title: Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines.
abstract: Identification of regulatory relationships between transcription factors (TFs) and their targets is a central problem in post-genomic biology. In this paper, we apply an approach based on the support vector machine (SVM) and gene-expression data to predict the regulatory interactions in Arabidopsis. A set of 125 experimentally validated TF-target interactions and 750 negative regulatory gene pairs are collected as the training data. Their expression profiles data at 79 experimental conditions are fed to the SVM to perform the prediction. Through the jackknife cross-validation test, we find that the overall prediction accuracy of our approach achieves 88.68%. Our approach could help to widen the understanding of Arabidopsis gene regulatory scheme and may offer a cost-effective alternative to construct the gene regulatory network.
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai, China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Yu
authors.forename: Taigang
authors.initials: T
authors.lastname: Liu
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Zheng
authors.forename: Zhongnan
authors.initials: Z
authors.lastname: Yang
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000235:genetics* / Q000378:metabolism; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D017343:Genes, Plant / Q000502:physiology; D015203:Reproducibility of Results; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D029681:Arabidopsis Proteins; D014157:Transcription Factors