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Protein & Peptide Letters


ISSN (Print): 0929-8665
ISSN (Online): 1875-5305

Support Vector Machine Based Prediction of Glutathione S-Transferase Proteins

Author(s): Nitish Kumar Mishra, Manish Kumar and G.P.S. Raghava

Volume 14, Issue 6, 2007

Page: [575 - 580] Pages: 6

DOI: 10.2174/092986607780990046

Price: $65


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 (

Keywords: GST protein, Support vector machine, artificial intelligence, sensitivity, specificity, correlation

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