Prediction and Classification of Human G-protein Coupled Receptors Based on Support Vector Machines  

作　　者：Yun-Fei Wang Huan Chen Yan-Hong Zhou 

机构地区：[1]Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan 430074, China.

出　　处：《Genomics, Proteomics & Bioinformatics》2005年第4期242-246,共5页基因组蛋白质组与生物信息学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.90203011 and 30370354);the Ministry of Education of China(No.505010 and CG2003-GA002).

摘　　要：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 GPCRs and 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.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 GPCRs and 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.

关 键 词：GPCR PREDICTION CLASSIFICATION SVM 

分 类 号：Q78[生物学—分子生物学]