Saha Sudipto, Zack Jyoti, Singh Balvinder, Raghava G P S
Institute of Microbial Technology, Chandigarh 160036, India.
Genomics Proteomics Bioinformatics. 2006 Nov;4(4):253-8. doi: 10.1016/S1672-0229(07)60006-0.
This study describes methods for predicting and classifying voltage-gated ion channels. Firstly, a standard support vector machine (SVM) method was developed for predicting ion channels by using amino acid composition and dipeptide composition, with an accuracy of 82.89% and 85.56%, respectively. The accuracy of this SVM method was improved from 85.56% to 89.11% when combined with PSI-BLAST similarity search. Then we developed an SVM method for classifying ion channels (potassium, sodium, calcium, and chloride) by using dipeptide composition and achieved an overall accuracy of 96.89%. We further achieved a classification accuracy of 97.78% by using a hybrid method that combines dipeptide-based SVM and hidden Markov model methods. A web server VGIchan has been developed for predicting and classifying voltage-gated ion channels using the above approaches.
本研究描述了预测和分类电压门控离子通道的方法。首先，开发了一种标准支持向量机（SVM）方法，通过使用氨基酸组成和二肽组成来预测离子通道，准确率分别为82.89%和85.56%。当与PSI-BLAST相似性搜索相结合时，该SVM方法的准确率从85.56%提高到了89.11%。然后，我们开发了一种利用二肽组成对离子通道（钾离子、钠离子、钙离子和氯离子通道）进行分类的SVM方法，总体准确率达到了96.89%。通过结合基于二肽的SVM和隐马尔可夫模型方法的混合方法，我们进一步实现了97.78%的分类准确率。已经开发了一个网络服务器VGIchan，用于使用上述方法预测和分类电压门控离子通道。
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