creators_name: Saha, Sudipto
creators_name: Zack, Jyoti
creators_name: Singh, Balvinder
creators_name: Raghava, G.P.S.
type: article
datestamp: 2011-11-29 09:13:30
lastmod: 2011-12-13 17:22:41
metadata_visibility: show
title: VGIchan: prediction and classification of voltage-gated ion channels.
ispublished: pub
subjects: QH426
full_text_status: restricted
note: Copyright of this article belongs to Beijing Genomics Institute
abstract: 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.
date: 2006-11
date_type: published
publication: Genomics, proteomics & bioinformatics 
volume: 4
number: 4
publisher: Beijing Genomics Institute
pagerange: 253-258
refereed: TRUE
issn: 1672-0229
related_url_url: http://www.imtech.res.in/raghava/reprints/vgichan.pdf
related_url_type: author
citation:   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     
document_url: http://crdd.osdd.net/open/636/1/raghava.pdf