Identification of Bacterial Sigma 70 Promoter Sequences Using Feature Subspace Based Ensemble Classifier
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Sigma promoter sequences in bacterial genomes are important due to their role in transcription initiation. Sigma70 is one of the most important and crucial sigma factors. In this paper, we address the problem of identification of σ 70 promoter sequences in bacterial genome. We propose iPromoterFSEn, a novel predictor for identification of σ 70 promoter sequences. Our proposed method is based on a feature subspace based ensemble classifier. A large set of of features extracted from the sequence of nucleotides are divided into subsets and each subset is given to individual single classifiers to learn. Based on the decisions of the ensemble an aggregate decision is made by the ensemble voting classifier. We tested our method on a standard benchmark dataset extracted from experimentally validated results. Experimental results shows that iPromoter-FSEn significantly improves over the state-of-the art σ 70 promoter sequence predictors. The accuracy and area under receiver operating characteristic curve of iPromoter-FSEn are 86.32% and 0.9319 respectively. We have also made our method readily available for use as an web application from: http://ipromoterfsen.pythonanywhere.com/server.
- M.Sc Thesis/Project