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

•SVM is explored to predict regulatory interactions in Arabidopsis. Experimentally validated regulatory relationships were collected as the positive samples.•Negative training samples were randomly selected TF–target pairs under some strategies.•Each gene pair was represented by incorporating the expression data and sequence information.•Through the jackknife test, our method reached an overall accuracy of 98.39% with the sensitivity of 94.88%, and the specificity of 93.82%.

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.

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