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Comparative Study
. 2005 Nov;3(4):242-6.
doi: 10.1016/s1672-0229(05)03034-2.

Prediction and classification of human G-protein coupled receptors based on support vector machines

Yun Fei Wang  1 , Huan Chen, Yan Hong Zhou
Affiliations
Comparative Study

Prediction and classification of human G-protein coupled receptors based on support vector machines

Yun Fei Wang et al. Genomics Proteomics Bioinformatics. 2005 Nov.

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 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.

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Figures

Fig. 1
Fig. 1
The length distributions of human GPCRs (A) and non-GPCRs (B).
Fig. 2
Fig. 2
The results of discriminating 653 human GPCRs from 10,845 non-GPCRs.
Fig. 3
Fig. 3
System flowchart for GPCR prediction and classification.
Fig. 4
Fig. 4
The μ value distribution of single amino acid composition features.
Fig. 5
Fig. 5
The μ value distribution of dipeptide composition features.
See this image and copyright information in PMC

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