Support vector machine based prediction of glutathione S-transferase proteins.

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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Abstract

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/).

Item Type:	Article
Additional Information:	Copyright of this article belongs to Bentham Science
Uncontrolled Keywords:	GST protein, Support vector machine, artificial intelligence, sensitivity, specificity, correlation
Subjects:	Q Science > QR Microbiology
Depositing User:	Dr. K.P.S.Sengar
Date Deposited:	30 Nov 2011 04:50
Last Modified:	09 Jan 2015 08:44
URI:	http://crdd.osdd.net/open/id/eprint/631

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