Prediction and Classification of Human G-protein Coupled Receptors Based on Support Vector Machines
- Author:
Wang YUN-FEI
1
;
Chen HUAN
;
Zhou YAN-HONG
Author Information
1. 华中理工大学
- Keywords:
GPCR;
prediction;
classification;
SVM
- From:
Genomics, Proteomics & Bioinformatics
2005;3(4):242-246
- CountryChina
- Language:Chinese
-
Abstract:
A computational system for the prediction and classification of human G-protein coupled receptors (GPCRs) has been developed based on the support vector machine (SVM) method and protein sequence information. The feature vectors used to develop the SVM prediction models consist of statistically significant features selected from single amino acid, dipeptide, and tripeptide compositions of protein sequences. Furthermore, the length distribution difference between GPCRsand non-GPCRs has also been exploited to improve the prediction performance.The testing results with annotated human protein sequences demonstrate that this system can get good performance for both prediction and classification of human GPCRs.
