title: A computational method of predicting regulatory interactions in Arabidopsis based on gene expression data and sequence information.
abstract: Inferring transcriptional regulatory interactions between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular system. In this paper, we introduced a computational method to predict regulatory interactions in Arabidopsis based on gene expression data and sequence information. Support vector machine (SVM) and Jackknife cross-validation test were employed to perform our method on a collected dataset including 178 positive samples and 1068 negative samples. Results showed that our method achieved an overall accuracy of 98.39% with the sensitivity of 94.88%, and the specificity of 93.82%, which suggested that our method can serve as a potential and cost-effective tool for predicting regulatory interactions in Arabidopsis.
authors.affiliation: Department of Applied Mathematics, Shanghai Institute of Technology, Shanghai 201418, China. Electronic address: xqyu@sit.edu.cn.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Yu
authors.affiliation: College of Information and Engineering, Dalian University, Dalian 116622, China.
authors.forename: Hongyun
authors.initials: H
authors.lastname: Gao
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai 200034, China.
authors.forename: Xiaoqi
authors.initials: X
authors.lastname: Zheng
authors.affiliation: Department of Mathematics, Bohai University, Jinzhou 121013, China.
authors.forename: Chun
authors.initials: C
authors.lastname: Li
authors.affiliation: Department of Mathematics, Shanghai Normal University, Shanghai 200034, China. Electronic address: jwang@shnu.edu.cn.
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
mesh_terms: D017360:Arabidopsis / Q000235:genetics*; D029681:Arabidopsis Proteins / Q000235:genetics*; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA; D060388:Support Vector Machine; D014157:Transcription Factors / Q000235:genetics*; D014158:Transcription, Genetic
chemical_list: D029681:Arabidopsis Proteins; D014157:Transcription Factors
keywords: Expression profile; Sequence information; Support vector machines; Transcription factor