Support vector machine based prediction of glutathione S-transferase proteins.
IR@IMTECH: CSIR-Institute of Microbial Technology, Chandigarh
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| Title |
Support vector machine based prediction of glutathione S-transferase proteins.
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| Creator |
Mishra, Nitish K
Kumar, Manish Raghava, G.P.S. |
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| Subject |
QR Microbiology
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| Description |
Glutathione S-transferase (GST) proteins play vital role in living organism that includes detoxification of exogenous and endogenous chemicals, survivability during stress condition. This paper describes a method developed for predicting GST proteins. We have used a dataset of 107 GST and 107 non-GST proteins for training and the performance of the method was evaluated with five-fold cross-validation technique. First a SVM based method has been developed using amino acid and dipeptide composition and achieved the maximum accuracy of 91.59% and 95.79% respectively. In addition we developed a SVM based method using tripeptide composition and achieved maximum accuracy 97.66% which is better than accuracy achieved by HMM based searching (96.26%). Based on above study a web-server GSTPred has been developed (http://www.imtech.res.in/raghava/gstpred/).
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| Publisher |
Bentham Science
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| Date |
2007
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://crdd.osdd.net/open/631/1/raghava2007.pdf
Mishra, Nitish K and Kumar, Manish and Raghava, G.P.S. (2007) Support vector machine based prediction of glutathione S-transferase proteins. Protein and peptide letters, 14 (6). pp. 575-580. ISSN 0929-8665 |
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| Relation |
http://www.benthamdirect.org/pages/content.php?PPL/2007/00000014/00000006/0012E.SGM
http://crdd.osdd.net/open/631/ |
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