title: Granular kernel trees with parallel genetic algorithms for drug activity comparisons.
abstract: With the growing interests of biological data prediction and chemical data prediction, more powerful and flexible kernels need to be designed so that the prior knowledge and relationships within data can be expressed effectively in kernel functions. In this paper, Granular Kernel Trees (GKTs) are proposed and parallel Genetic Algorithms (GAs) are used to optimise the parameters of GKTs. In applications, SVMs with new kernel trees are employed for drug activity comparisons. The experimental results show that GKTs and evolutionary GKTs can achieve better performances than traditional RBF kernels in terms of prediction accuracy.
authors.affiliation: Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA. cscbxjx@cs.gsu.edu
authors.forename: Bo
authors.initials: B
authors.lastname: Jin
authors.forename: Yan-Qing
authors.initials: YQ
authors.lastname: Zhang
authors.forename: Binghe
authors.initials: B
authors.lastname: Wang
mesh_terms: D001185:Artificial Intelligence; D005075:Biological Evolution; D004791:Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D004926:Escherichia coli / Q000201:enzymology; D016247:Information Storage and Retrieval; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D013762:Tetrahydrofolate Dehydrogenase / Q000187:drug effects
chemical_list: D004791:Enzyme Inhibitors; D004364:Pharmaceutical Preparations; D013762:Tetrahydrofolate Dehydrogenase