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
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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.
| Item Type: | Article |
|---|---|
| Additional Information: | Copyright of this article belongs to Beijing Genomics Institute |
| Subjects: | Q Science > QH Natural history > QH426 Genetics |
| Depositing User: | Dr. K.P.S.Sengar |
| Date Deposited: | 29 Nov 2011 09:13 |
| Last Modified: | 13 Dec 2011 17:22 |
| URI: | http://crdd.osdd.net/open/id/eprint/636 |
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