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iPromoter-FSEn: Identification of bacterial σ70 promoter sequences using feature subspace based ensemble classifier

机译：iPROMoter-FSEN：使用特征子空间基于集合分类器的细菌σ70启动子序列的鉴定

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摘要

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.
机译：由于其在转录开始中的作用，细菌基因组中的Sigma启动子序列很重要。 Sigma 70是最重要和最重要的Sigma因素之一。在本文中，我们解决了细菌基因组中σ70启动子序列的鉴定问题。我们提出IPROMoter-FSEN，一种用于鉴定σ70启动子序列的新型预测因子。我们所提出的方法基于基于子空间的集合分类器。从核苷酸序列中提取的大量特征被分成子集，并且每个子集被赋予各个单个分类器来学习。基于集合的决定，集合投票分类器进行了总决定。我们在从实验验证结果中提取的标准基准数据集上测试了我们的方法。实验结果表明，IPROMoter-FSEN显着改善了最先进的σ70启动子序列预测因子。 IPROMoter-FSEN的接收器操作特性曲线下的准确度和面积分别为86.32％和0.9319。我们还使我们的方法随时可用作Web应用程序：http：//iproomoterfsen.pythonanywhere.com/server。

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