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.
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References
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Jin, B., Zhang, YQ. (2006). Evolutionary Construction of Granular Kernel Trees for Cyclooxygenase-2 Inhibitor Activity Comparison. In: Priami, C., Hu, X., Pan, Y., Lin, T.Y. (eds) Transactions on Computational Systems Biology V. Lecture Notes in Computer Science(), vol 4070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11790105_3
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DOI: https://doi.org/10.1007/11790105_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36048-3
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