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

Yanbin Wang,Liping Li,Tonghai Jiang,Li Cheng,Zhuhong You,Xi Zhou,Libo Zhang,Xiao Li

doi:10.1155/2018/4216813

Abstract

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.

Highlights

Most important molecular processes in cells are performed by different types of protein interactions
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
We propose an efficient protein evolution feature extraction scheme, which used a deep learning algorithm combined with Legendre moments (LMs) and position weight matrix (PWM)

Summary

Introduction

Most important molecular processes in cells are performed by different types of protein interactions. The above information can only represent each specific protein sequence but does not contain knowledge related to protein interactions Even these methods combined with advanced classification algorithms have a difficulty in producing enough accuracy. We hypothesize that there is a potential relationship between the conservation of amino acid residues during evolution and the interaction of proteins Based on this hypothesis, we propose an efficient protein evolution feature extraction scheme, which used a deep learning algorithm combined with Legendre moments (LMs) and position weight matrix (PWM). (2) We have abandoned the traditional materialized information and structural information, considered the evolutionary information associated with PPIs as a feature of the protein sequence, and proposed a feature extraction strategy to quickly and efficiently extract the evolutionary information of the protein and improve the prediction performance.

Related Work

Materials and Methodology

PCVM xd bh2

Results

Conclusion

Conflicts of Interest