Comput Biol Chem - A computational method of predicting regulatory interactions in Arabidopsis based on gene expression data and sequence information.

Tópicos

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Resumo

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.

Resumo Limpo

infer transcript regulatori interact transcript factor tfs target utmost import understand complex regulatori mechan cellular system paper introduc comput method predict regulatori interact arabidopsi base gene express data sequenc inform support vector machin svm jackknif crossvalid test employ perform method collect dataset includ posit sampl negat sampl result show method achiev overal accuraci sensit specif suggest method can serv potenti costeffect tool predict regulatori interact arabidopsi

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