Provider: Ingenta Connect Database: Ingenta Connect Content: application/x-research-info-systems TY - ABST AU - Nitish Kumar Mishra AU - Manish Kumar AU - G.P.S. Raghava TI - Support Vector Machine Based Prediction of Glutathione S-Transferase Proteins JO - Protein and Peptide Letters PY - 2007-06-01T00:00:00/// VL - 14 IS - 6 SP - 575 EP - 580 KW - GST protein KW - correlation KW - Support vector machine KW - sensitivity KW - specificity KW - artificial intelligence N2 - 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/).





UR - https://www.ingentaconnect.com/content/ben/ppl/2007/00000014/00000006/art00012 M3 - doi:10.2174/092986607780990046 UR - https://doi.org/10.2174/092986607780990046 ER -