title: Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors.
abstract: Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori, we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. lg_wong@foxmail.com.
authors.forename: Leon
authors.initials: L
authors.lastname: Wong
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. zhuhongyou@cumt.edu.cn.
authors.forename: Zhu-Hong
authors.initials: ZH
authors.lastname: You
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. mingz@szu.edu.cn.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Ming
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. lijq@szu.edu.cn.
authors.forename: Jianqiang
authors.initials: J
authors.lastname: Li
authors.affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. xingchen@amss.ac.cn.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. yahuang1991@gmail.com.
authors.forename: Yu-An
authors.initials: YA
authors.lastname: Huang
mesh_terms: D016480:Helicobacter pylori / Q000235:genetics / Q000378:metabolism; D006801:Humans; D011485:Protein Binding; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D012984:Software
chemical_list: D020543:Proteome
keywords: Local Phase Quantization; Physicochemical Property Response Matrix (PR); Rotation Forest; protein-protein interaction