title: Improving hot region prediction by parameter optimization of density clustering in PPI.
abstract: This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius.
authors.affiliation: School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China. Electronic address: hujing@wust.edu.cn.
authors.forename: Jing
authors.initials: J
authors.lastname: Hu
authors.affiliation: School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan, China. Electronic address: xiaolong.zhang@wust.edu.cn.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps / Q000235:genetics*
keywords: Clustering; Density; Hot region; Parameter selection; Protein–protein interaction