Predicting protein interactions using a deep learning method-stacked sparse autoencoder combined with a probabilistic classification vector machine

Protein-protein interactions (PPIs), as an important molecular process within cells, are of pivotal importance in the biochemical function of cells. Although high-throughput experimental techniques have matured, enabling researchers to detect large amounts of PPIs, it has unavoidable disadvantages, such as having a high cost and being time consuming. Recent studies have demonstrated that PPIs can be efficiently detected by computational methods. Therefore, in this study, we propose a novel computational method to predict PPIs using only protein sequence information. This method was developed based on a deep learning algorithm-stacked sparse autoencoder (SSAE) combined with a Legendre moment (LM) feature extraction technique. Finally, a probabilistic classification vector machine (PCVM) classifier is used to implement PPI prediction. The proposed method was performed on human, unbalanced-human, H. pylori, and S. cerevisiae datasets with 5-fold cross-validation and yielded very high predictive accuracies of 98.58%, 97.71%, 93.76%, and 96.55%, respectively. To further evaluate the performance of our method, we compare it with the support vector machine- (SVM-) based method. The experimental results indicate that the PCVM-based method is obviously preferable to the SVM-based method. Our results have proven that the proposed method is practical, effective, and robust.