PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein–Protein Interactions from Protein Sequences

Abstract

Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network

“…Many sequencebased machine learning methods (Huang et al, 2016;An et al, 2017;Wang et al, 2017a,b;You et al, 2017) have been developed. Based on the primary sequences of proteins, they use machine learning algorithms, such as Neural Network (Wang et al, 2017b), Support Vector Machine (SVM) (Wang et al, 2017a), and rotation forest (You et al, 2017) to predict proteinprotein interactions.…”

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

“…Ding et al proposed a novel protein sequence representation method based on a matrix to predict PPIs via an ensemble classification method [17]. Wang et al proposed a computational method based on a probabilistic classification vector machine (PCVM) model and a Zernike moment (ZM) descriptor to identify PPIs from amino acids sequences [18]. Lei et al employed the NABCAM (the neighbor affinity-based core-attachment method) to identify protein complexes from dynamic PPI networks [19].…”

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

“…You et al proposed a prediction framework for detecting PPIs using a low-rank approximation-kernel extreme learning machine [34]. Several other sequence-based computational methods have been reported in previous work [35][36][37][38]. These sequence-based methods show that the individual information of the amino acid sequence is sufficient to determine the interaction of the protein.…”