@article{open631,
          volume = {14},
          number = {6},
          author = {Nitish K Mishra and Manish Kumar and G.P.S. Raghava},
            note = {Copyright of this article belongs to  Bentham Science},
           title = {Support vector machine based prediction of glutathione S-transferase proteins.},
       publisher = {Bentham Science},
            year = {2007},
         journal = {Protein and peptide letters},
           pages = {575--580},
        keywords = {GST protein, Support vector machine, artificial intelligence, sensitivity, specificity, correlation },
             url = {http://crdd.osdd.net/open/631/},
        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/).}
}