A computational method of predicting regulatory interactions in Arabidopsis based on gene expression data and sequence information

Comput Biol Chem. 2014 Aug:51:36-41. doi: 10.1016/j.compbiolchem.2014.04.003. Epub 2014 May 9.

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.

Keywords: Expression profile; Sequence information; Support vector machines; Transcription factor.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Arabidopsis / genetics*
  • Arabidopsis Proteins / genetics*
  • Gene Expression Profiling
  • Gene Expression Regulation, Plant*
  • Gene Regulatory Networks
  • Sensitivity and Specificity
  • Sequence Analysis, DNA
  • Support Vector Machine*
  • Transcription Factors / genetics*
  • Transcription, Genetic

Substances

  • Arabidopsis Proteins
  • Transcription Factors