title: Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.
abstract: Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.
authors.affiliation: School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, Hubei, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Hu
authors.affiliation: School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, Hubei, China. Electronic address: xiaolong.zhang@wust.edu.cn.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, Hubei, China.
authors.forename: Xiaoming
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Technology, Michigan Technological University, Houghton, MI 49931, USA. Electronic address: jinshant@mtu.edu.
authors.forename: Jinshan
authors.initials: J
authors.lastname: Tang
mesh_terms: D008956:Models, Chemical; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000235:genetics; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins
keywords: Clustering; Density-based; Feature-based classification; Hot region; Protein–protein interaction