Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach. (7th February 2019)

• Record Type: Journal Article
• Authors: Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin
• Is Part Of: Journal of theoretical biology. Volume 462(2019)
• Page Start: 329
• Page End: 346
• Publication Date: 2019-02-07
• Subjects: Protein–protein interactions -- Pseudo-amino acid composition -- Multi-information fusion -- Two-dimensional wavelet denoising -- Support vector machine -- Machine learning; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2018.11.011
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: A new method (PPIs-WDSVM) for prediction protein–protein interactions. The protein sequence features are extracted by fusing the PseAAC, AC and EBGW methods. 2-D wavelet denoising can effectively remove the redundant information in the protein sequences. We compared the effect of the five different classifiers on the results. The proposed method increases the prediction performance over several methods. Abstract: Research on protein–protein interactions (PPIs) not only helps to reveal the nature of life activities but also plays a driving role in understanding the mechanisms of disease activity and the development of effective drugs. The rapid development of machine learning provides new opportunities and challenges for understanding the mechanism of PPIs. It plays an important role in the field of proteomics research. In recent years, an increasing number of computational methods for predicting PPIs have been developed. This paper proposes a new method for predicting PPIs based on multi-information fusion. First, the pseudo-amino acid composition (PseAAC), auto-covariance (AC) and encoding based on grouped weight (EBGW) methods are used to extract the features of protein sequences, and the extracted three groups of feature vectors were fused. Secondly, the fused feature vectors are denoised by two-dimensional (2-D) wavelet denoising. Finally, the denoised feature vectors are input to the support vector machine (SVM) classifier to predict the PPIs. The ACC of PPIs of Helicobacter pylori ( H. pylori ) and Saccharomyces cerevisiae ( S. cerevisiae ) datasets were 95.97% and 95.55% by 5-fold cross-validation test and compared with other prediction methods. The experimental results show that the proposed multi-information fusion prediction method can effectively improve the prediction performance of PPIs. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/PPIs-WDSVM/ .