Article Abstract

	Title:	Granular Kernel Trees with parallel Genetic Algorithms for drug activity comparisons
	Author:	Bo Jin, Yan-Qing Zhang, Binghe Wang
	Address:	Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA. ' Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA. ' Department of Chemistry and Center for Biotechnology and Drug Design, Georgia State University, Atlanta, GA 30302-4098, USA
	Journal:	International Journal of Data Mining and Bioinformatics 2007 - Vol. 1, No.3  pp. 270 - 285
	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.
	Keywords:	kernel design; support vector machines; SVMs; granular kernel trees; GKTs; genetic algorithms; parallel GAs; drug activity comparisons; data mining; bioinformatics; prediction accuracy; biological data prediction; chemical data prediction.
	DOI:	10.1504/IJDMB.2007.011613
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