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An efficient CNN-based classification on G-protein Coupled Receptors using TF-IDF and N-gram

Protein sequence classification is increasingly crucial in the current “biological information sciences” epoch, where researchers hammer at functional genomics and proteomics technologies for predicting the function of large-scale new proteins. This has sparked interest in the methods which do not rely on traditional sequence alignment, but prefer machine learning approaches. In this paper, we present a Convolutional Neural Network (CNN) based method to perform the classification on the different levels of G-protein Coupled Receptors (GPCRs). The method is implemented in conjunction with an improved feature extraction method and TF-IDF feature weighting strategy. Experimental results indicate that the proposed method makes significant improvements over previous methods, which attains an accuracy of up to 98.34%, 98.13% and 96.47% in the classification of family level, subfamily level I and II, respectively. In comparison to the other well-known classification methods for GPCRs, the classification error rate of the proposed method is reduced by of at least 55.14% (family level), 72.86% (level I) and 52.63% (Level II).