Wang Yun Fei, Chen Huan, Zhou Yan Hong
Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan, 430074, China.
Genomics Proteomics Bioinformatics. 2005 Nov;3(4):242-6. doi: 10.1016/s1672-0229(05)03034-2.
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.
基于支持向量机(SVM)方法和蛋白质序列信息,开发了一种用于预测和分类人类G蛋白偶联受体(GPCR)的计算系统。用于开发SVM预测模型的特征向量由从蛋白质序列的单个氨基酸、二肽和三肽组成中选择的具有统计学意义的特征组成。此外,还利用了GPCR与非GPCR之间的长度分布差异来提高预测性能。对带注释的人类蛋白质序列的测试结果表明,该系统在人类GPCR的预测和分类方面均能取得良好的性能。