A new algorithm based on complex wavelet transform for protein sequence classification

Abstract

With the approach of post-genome era, proteomics is becoming an important research domain in the life science. With the rapid development of modern biological science and technology, protein sequence data are emerging at an explosive pace. According to this, the classification of protein sequences becomes more and more important in the present medical research. The protein plays the key roles in many diseases. In order to improve the accuracy and efficiency of the protein sequence classification, a new algorithm based on the complex wavelet transform is presented. The complex wavelet transform can extract features of signals accurately based on its multiresolution characteristics and shift invariance. The method presented that the features extracted from the complex wavelet coefficients can be used to represent the original sequences. The feature vectors extracted from the complex wavelet coefficients can be classified by PNN more exactly than the methods based on DWT. We employ two kinds of complex wavelet in the conducted experiments and experimental results show that the classification rate based on the complex wavelet is improved evidently.