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Appl. Sci. 2018, 8(1), 89; doi:10.3390/app8010089

An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information

1
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
2
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
Co-first author.
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 2 January 2018 / Accepted: 3 January 2018 / Published: 10 January 2018
(This article belongs to the Section Chemistry)
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Abstract

Identifying protein–protein interactions (PPIs) is crucial to comprehend various biological processes in cells. Although high-throughput techniques generate many PPI data for various species, they are only a petty minority of the entire PPI network. Furthermore, these approaches are costly and time-consuming and have a high error rate. Therefore, it is necessary to design computational methods for efficiently detecting PPIs. In this study, a random projection ensemble classifier (RPEC) was explored to identify novel PPIs using evolutionary information contained in protein amino acid sequences. The evolutionary information was obtained from a position-specific scoring matrix (PSSM) generated from PSI-BLAST. A novel feature fusion scheme was then developed by combining discrete cosine transform (DCT), fast Fourier transform (FFT), and singular value decomposition (SVD). Finally, via the random projection ensemble classifier, the performance of the presented approach was evaluated on Yeast, Human, and H. pylori PPI datasets using 5-fold cross-validation. Our approach achieved high prediction accuracies of 95.64%, 96.59%, and 87.62%, respectively, effectively outperforming other existing methods. Generally speaking, our approach is quite promising and supplies a practical and effective method for predicting novel PPIs. View Full-Text
Keywords: protein–protein interactions; position-specific scoring matrix; random projection ensemble classifier; support vector machine protein–protein interactions; position-specific scoring matrix; random projection ensemble classifier; support vector machine
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Song, X.-Y.; Chen, Z.-H.; Sun, X.-Y.; You, Z.-H.; Li, L.-P.; Zhao, Y. An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information. Appl. Sci. 2018, 8, 89.

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