Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

Abstract

Predicting protein–protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high‐throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM‐BiGP that combines the relevance vector machine (RVM) model and Bi‐gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi‐gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five‐fold cross‐validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state‐of‐the‐art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM‐BiGP method is significantly better than the SVM‐based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM‐BiGP‐PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/.

Computational Methods for Predicting Protein‐Protein Interactions Using Various Protein Features

“…An et al. achieved 94.57% and 90.57% on the S. cerevisiae and the H. pylori dataset, respectively; also, a 97.15% accuracy on an imbalanced yeast dataset (An et al., ). Wei showed over 81% accuracy using different features on the Negatome and the DIP dataset (Blohm et al., ; L. Wei et al., ; Xenarios et al., ).…”

“…Algorithms used include support vector machine (SVM; Bock & Gough, ; Y. Guo et al., ; X. Liu et al., ; Martin et al., ; Shen et al., ; Y. Z. Zhou et al., ), relevance vector machine (An et al., ), random forest (X.‐W. Chen & Liu, ; Ding et al., ; You et al., ), rotation forest (L. Wong et al., ), linear discriminant classifier and cloud points (Nanni, ), relaxed variable kernel density estimator (RVKDE; C.‐Y.…”

FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification

“…Tables 4-6 compare the prediction performance by the proposed method (FCTP-WSRC) and some outstanding works on the H. pylori, Yeast and Human dataset. Table 4 describes the average accuracies of other seven methods including HKNN (Nanni, 2005), Signature products (Shawn et al, 2005), Ensemble of HKNN (Nanni and Lumini, 2006), PCA+ELM (You et al, 2013), WSRC+GE (Nanni and Lumini, 2006), HOG +SVD+RF (Ding et al, 2016), and RVM+BiGP (An et al, 2016). Table 5 describes the average accuracies of other seven methods including LDA+RF (Xiao-Yong et al, 2010), LDA+RoF (Xiao-Yong et al, 2010), AC+RF (Xiao-Yong et al, 2010), AC+RoF [41), WSRC+GE (Huang et al, 2016a), and HOG+SVD+RF (Ding et al, 2016).…”

“…Huang et al predicts PPIs utilizing different information sources such as tertiary structure of proteins, phylogenetic profiles, and protein domains (De-Shuang and Chun-Hou, 2006;De-Shuang and Ji-Xiang, 2008). However, these computational methods require prior knowledge of the target protein (An et al, 2016). In recent years, protein sequencebased methods (Yu et al, 2017) are becoming the most widely applied technique for predicting PPIs due to the availability of protein sequence data.…”

Exploring Mouse Protein Function via Multiple Approaches

“…There are many computational models for predicting protein-protein interactions [22–24]. The commonly accepted hypothesis (called guilt-by-association (GBA) [25]) is that proteins are more likely to share identical or similar functions with interacting proteins than with non-interacting proteins.…”