title: Prediction of Protein-Protein Interactions with Physicochemical Descriptors and Wavelet Transform via Random Forests.
abstract: Protein-protein interactions (PPIs) provide valuable insight into the inner workings of cells, and it is significant to study the network of PPIs. It is vitally important to develop an automated method as a high-throughput tool to timely predict PPIs. Based on the physicochemical descriptors, a protein was converted into several digital signals, and then wavelet transform was used to analyze them. With such a formulation frame to represent the samples of protein sequences, the random forests algorithm was adopted to conduct prediction. The results on a large-scale independent-test data set show that the proposed model can achieve a good performance with an accuracy value of about 0.86 and a geometric mean value of about 0.85. Therefore, it can be a usefully supplementary tool for PPI prediction. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI_RF.
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China School of Computer Science, University of Birmingham, Edgbaston, Birmingham, UK jjh163yx@163.com.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Jia
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China Gordon Life Science Institute, Boston, MA, USA.
authors.forename: Xuan
authors.initials: X
authors.lastname: Xiao
authors.affiliation: School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
authors.forename: Bingxiang
authors.initials: B
authors.lastname: Liu
mesh_terms: D055598:Chemical Phenomena; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000378:metabolism*; D058067:Wavelet Analysis
chemical_list: D011506:Proteins
keywords: physicochemical descriptor; protein–protein interaction; random forest; wavelet transform