An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences
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Lei Wang1,5,*, Zhu-Hong You2,*, Xing Chen3, Jian-Qiang Li4, Xin Yan6, Wei Zhang5, Yu-An Huang4
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
2Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
3School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
4College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
5College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China
6School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China
*Joint First Authors
Zhu-Hong You, email: email@example.com
Xing Chen, email: firstname.lastname@example.org
Keywords: disease, position-specific scoring matrix, multiple sequences alignments, cancer
Received: October 11, 2016 Accepted: November 15, 2016 Published: December 22, 2016
Protein–Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URLfor academic use.
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