title: Support vector machine based prediction of glutathione S-transferase proteins.
creator: Mishra, Nitish K
creator: Kumar, Manish
creator: Raghava, G.P.S.
subject: QR Microbiology
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/).
publisher: Bentham Science
date: 2007
type: Article
type: PeerReviewed
format: application/pdf
identifier: http://crdd.osdd.net/open/631/1/raghava2007.pdf
relation: http://www.benthamdirect.org/pages/content.php?PPL/2007/00000014/00000006/0012E.SGM
identifier:   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     
relation: http://crdd.osdd.net/open/631/