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首页> 美国卫生研究院文献>Journal of Zhejiang University. Science. B >Science Letters: EHPred: an SVM-based method for epoxide hydrolases recognition and classification
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Science Letters: EHPred: an SVM-based method for epoxide hydrolases recognition and classification

机译：科学快报：EHPred：基于SVM的环氧水解酶识别和分类方法

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摘要

A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classifiers were built using three different feature vectors extracted from the primary sequence of EHs: the amino acid composition (AAC), the dipeptide composition (DPC), and the pseudo-amino acid composition (PAAC). Validated by 5-fold cross tests, the first layer SVM classifier can differentiate EHs and non-EHs with an accuracy of 94.2% and has a Matthew’s correlation coefficient (MCC) of 0.84. Using 2-fold cross validation, PAAC-based second layer SVM can further classify EH subfamilies with an overall accuracy of 90.7% and MCC of 0.87 as compared to AAC (80.0%) and DPC (84.9%). A program called EHPred has also been developed to assist readers to recognize EHs and to classify their subfamilies using primary protein sequences with greater accuracy.
机译：已经开发了一种基于支持向量机（SVM）的两层方法，以区分环氧化物水解酶（EH）与其他酶，并使用其一级蛋白质序列对其亚家族进行分类。使用从EHs的主要序列中提取的三个不同特征向量构建SVM分类器：氨基酸组成（AAC），二肽组成（DPC）和伪氨基酸组成（PAAC）。经过5次交叉测试的验证，第一层SVM分类器可以区分EH和非EH，准确度为94.2％，马修相关系数（MCC）为0.84。使用2倍交叉验证，与AAC（80.0％）和DPC（84.9％）相比，基于PAAC的第二层SVM可以进一步对EH亚科进行分类，总体准确度为90.7％，MCC为0.87。还开发了一个名为EHPred的程序，以帮助读者识别EHs并使用一级蛋白质序列更准确地对其亚家族进行分类。

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