VGIchan: Prediction and Classification of Voltage-Gated Ion Channels

• 作 者: Sudipto Saha Jyoti Zack Balvin
• 机 构: Institute of Microbial Technology
• 出 处: 基因组、蛋白质组与生物信息学报（英文版）
• 发表日期: 2004年期
• 关键词: ion channels prediction VGIchan SVM HMM
• 基金项目: the Council of Scientific and Industrial Research (CSIR);the Department of Biotechnology, Government of India

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 PSIBLAST 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 dipeptidebased 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. VGIchan is freely available at www.imtech.res.in/raghava/vgichan/.