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VGIchan: prediction and classification of voltage-gated ion channels.

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title VGIchan: prediction and classification of voltage-gated ion channels.
 
Creator Saha, Sudipto
Zack, Jyoti
Singh, Balvinder
Raghava, G.P.S.
 
Subject QH426 Genetics
 
Description 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.
 
Publisher Beijing Genomics Institute
 
Date 2006-11
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/636/1/raghava.pdf
Saha, Sudipto and Zack, Jyoti and Singh, Balvinder and Raghava, G.P.S. (2006) VGIchan: prediction and classification of voltage-gated ion channels. Genomics, proteomics & bioinformatics , 4 (4). pp. 253-258. ISSN 1672-0229
 
Relation http://crdd.osdd.net/open/636/