Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising
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Bin Yu1,2,3,*, Shan Li1,2,*, Wen-Ying Qiu1,2,*, Cheng Chen1,2, Rui-Xin Chen1,2, Lei Wang4, Ming-Hui Wang1,2 and Yan Zhang2,5
1College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
2Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
3CAS Key Laboratory of Geospace Environment, Department of Geophysics and Planetary Science, University of Science and Technology of China, Hefei 230026, China
4Key Laboratory of Eco-Chemical Engineering, Ministry of Education, Laboratory of Inorganic Synthesis and Applied Chemistry, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
5College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
*These authors contributed equally to this work and are joint First Authors
Bin Yu, email: email@example.com
Keywords: apoptosis protein subcellular location; pseudo-amino acid composition; pseudo-position specific scoring matrix; two-dimensional wavelet denoising; support vector machine
Received: September 04, 2017 Accepted: October 30, 2017 Published: November 21, 2017
Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.
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