Improved method for predicting β-turn using support vector machine
Improved method for predicting β-turn using support vector machine
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DOI：
10.1093/bioinformatics/bti358
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发表时间：
2005-05-15
期刊：
影响因子：
5.8
通讯作者：
Welsh, WJ
中科院分区：
文献类型：
--
作者：
Zhang, QD;Yoon, SJ;Welsh, WJ
动机：基于各种计算方案，已经开发了许多用于预测蛋白质中β-转角的方法。在这里，我们介绍了一种新的β-转角预测方法，该方法使用支持向量机（SVM）算法与预测的二级结构信息。从SVM的各种参数进行了调整，以达到最佳的预测performance.Results：SVM方法取得了优异的性能，测量的马修斯相关系数（MCC = 0.45）使用7倍交叉验证数据库上的426个非同源蛋白质链。据我们所知，这个MCC值是迄今为止预测β转弯所达到的最高值。整体预测精度Q（总）为77.3%，是现有预测方法中最好的。在其独特的吸引力的功能，本SVM方法避免过度训练和压缩信息，并提供了一个预测的可靠性指标。
Motivation: Numerous methods for predicting beta-turns in proteins have been developed based on various computational schemes. Here, we introduce a new method of beta-turn prediction that uses the support vector machine (SVM) algorithm together with predicted secondary structure information. Various parameters from the SVM have been adjusted to achieve optimal prediction performance.Results: The SVM method achieved excellent performance as measured by the Matthews correlation coefficient (MCC = 0.45) using a 7-fold cross validation on a database of 426 non-homologous protein chains. To our best knowledge, this MCC value is the highest achieved so far for predicting beta-turn. The overall prediction accuracy Q(total) was 77.3%, which is the best among the existing prediction methods. Among its unique attractive features, the present SVM method avoids overtraining and compresses information and provides a predicted reliability index.