Prediction of G-protein-coupled receptor classes based on the concept of Chou’s pseudo amino acid composition: An approach from discrete wavelet transform
Section snippets
Data sets
Three data sets were used in this work. The first data set contains 1238 GPCR sequences that can be classified into three major families: 1103 class A–rhodopsin like, 84 class B–secretin like, and 51 class C–metabotropic/glutamate/pheromone [13]. The average sequence identity percentages for classes A, B, and C are 18.05%, 22.67%, and 26.94%, respectively [13]. The second data set used to recognize the subfamilies of class A–rhodopsin like was generated by Strope and Moriyama [42]. It contains
Selecting wavelet functions
Based on different basis functions, the wavelets have different families; every family has its quality fit for different signal and has different results. Because the characteristics of the analyzing wavelet control the performance of the WT, the better the analyzing wavelet matches the underlying structure in the signal, the more concise and sparse the WT representation. It has been clearly stated that the amount of signal compression and the reconstruction quality are highly dependent on the
Conclusion
In this work, a novel predictive method has been proposed for the prediction of GPCRs by coupling SVM with DWT. The predictive results demonstrate that WT can reduce dimension of input vector, improve calculating efficiency, and effectively extract important classified information. In comparison with previous literature methods, the predictive performance was significantly enhanced, indicating that the current method is an effective tool for the prediction of GPCRs. The establishment of such a
Acknowledgments
This work was supported by grants from the National Natural Science Foundation of China (20605010, 20865003, and 20805023), the Jiangxi Province Natural Science Foundation (2007JZH2644), and the Opening Foundation of State Key Laboratory of Chem/Biosensing and Chemometrics of Hunan University (2006022).
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