VGIchan: prediction and classification of voltage-gated ion channels.
IR@IMTECH: CSIR-Institute of Microbial Technology, Chandigarh
View Archive Info| Field | Value | |
| Title |
VGIchan: prediction and classification of voltage-gated ion channels.
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| Creator |
Saha, Sudipto
Zack, Jyoti Singh, Balvinder Raghava, G.P.S. |
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| Subject |
QH426 Genetics
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| 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.
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| Publisher |
Beijing Genomics Institute
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| Date |
2006-11
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| 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 |
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| Relation |
http://crdd.osdd.net/open/636/
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