Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine

الملخص EN

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.