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Identifying protein-protein interactions (PPIs) plays a vital role in a number of biological activities such as signal transduction, transcriptional regulation, and apoptosis. Although advances in high-throughput technologies have generated large amounts of PPI data for different species, they only cover a small part of the entire PPI network. Furthermore, traditional experimental methods are generally expensive, time-consuming, tedious, and prone to high false-positive rates. Therefore, to overcome this problem, it is necessary to develop a novel computational method for predicting PPIs. In this article, we propose an efficient computational method to detect protein-protein interactions using only protein sequence information, which integrates the MatPCA feature extraction algorithm and the weighted sparse representation classifier. As a result, when predicting PPIs on yeast, human, and H. pylori datasets, the proposed method achieves superior prediction performance with an average accuracy of 94.55%, 97.48%, and 83.64%, respectively. These experimental results further illustrate that the proposed method is reliable and robust in predicting PPIs, which can be regarded as a useful complement to the experimental method.

mathematical problems in engineering

Prediction of Protein-Protein Interactions from Protein Sequences by Combining MatPCA Feature Extraction Algorithms and Weighted Sparse Representation Models