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 -