title: Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm.
abstract: Studying the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. It is vitally important to develop an automated, high-throughput tool that efficiently predicts protein-protein interactions. This study proposes a new model for PPI prediction based on the concept of chaos game representation and the wavelet transform, which means that a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers. The advantage of using chaos game representation and the wavelet transform to formulate the protein sequence is that it can more effectively reflect its overall sequence-order characteristics than the conventional correlation factors. Using such a formulation frame to represent the protein sequences means that the random forest algorithm can be used to conduct the prediction. The results for a large-scale independent test dataset show that the proposed model can achieve an excellent performance with an accuracy value of about 0.86 and a geometry mean value of about 0.85. The model is therefore a useful supplementary tool for PPI predictions. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI.
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China jjh163yx@163.com.
authors.forename: J H
authors.initials: JH
authors.lastname: Jia
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
authors.forename: Z
authors.initials: Z
authors.lastname: Liu
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
authors.forename: X
authors.initials: X
authors.lastname: Chen
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
authors.forename: X
authors.initials: X
authors.lastname: Xiao
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
authors.forename: B X
authors.initials: BX
authors.lastname: Liu
mesh_terms: D001426:Bacterial Proteins / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D066264:Datasets as Topic; D016480:Helicobacter pylori / Q000378:metabolism; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D058067:Wavelet Analysis
chemical_list: D001426:Bacterial Proteins; D029701:Saccharomyces cerevisiae Proteins