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Evolutionary Construction of Granular Kernel Trees for Cyclooxygenase-2 Inhibitor Activity Comparison

  • Bo Jin
  • Yan-Qing Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4070)

Abstract

With the growing interest of biological data prediction and chemical data prediction, more and more complicated kernels are designed to integrate data structures and relationships. We proposed a kind of evolutionary granular kernel trees (EGKTs) for drug activity comparisons [1]. In EGKTs, feature granules and tree structures are predefined based on the possible substituent locations. In this paper, we present a new system to evolve the structures of granular kernel trees (GKTs) in the case that we lack knowledge to predefine kernel trees. The new granular kernel tree structure evolving system is used for cyclooxygenase-2 inhibitor activity comparison. Experimental results show that the new system can achieve better performance than SVMs with traditional RBF kernels in terms of prediction accuracy.

Keywords

Support Vector Machine Prediction Accuracy Training Accuracy Graph Kernel String Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo Jin
    • 1
  • Yan-Qing Zhang
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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