iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier
| Autor: | Usma Aktar, Swakkhar Shatabda, Md. Rafsan Jani, Md. Siddiqur Rahman |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
0303 health sciences Receiver operating characteristic Sequence analysis business.industry Sigma Pattern recognition Bacterial genome size Biology 01 natural sciences Transcription initiation 03 medical and health sciences Sigma factor Genetics Artificial intelligence business Classifier (UML) Subspace topology 030304 developmental biology 010606 plant biology & botany |
| Zdroj: | Genomics. 111:1160-1166 |
| ISSN: | 0888-7543 |
| DOI: | 10.1016/j.ygeno.2018.07.011 |
| Popis: | Sigma promoter sequences in bacterial genomes are important due to their role in transcription initiation. Sigma 70 is one of the most important and crucial sigma factors. In this paper, we address the problem of identification of σ70 promoter sequences in bacterial genome. We propose iPromoter-FSEn, a novel predictor for identification of σ70 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 σ70 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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