Evolutionary voting kernel machines for cyclooxygenase-2 inhibitor activity comparisons

Abstract

With the growing interest of biological data prediction and chemical data prediction, more complicated kernels are designed to measure data similarities. In (1), we proposed a kind of evolutionary granular kernel trees (EGKTs) for drug activity comparisons. In EGKTs, feature granules and tree structures are predefined based on the possible substituent locations. In (2), we proposed a granular kernel tree structure evolving system (GKTSES) to evolve the structures of GKTs in the case that we lack knowledge to predefine kernel trees. In this paper, evolutionary voting kernel machines (EVKMs) are presented based on GKTSES. Experimental results show that EVKMs are more stable than GKTSES in cyclooxygenase-2 inhibitor activity comparisons. Index Terms—Drug activity comparisons, kernel, genetic algorithms, granular kernel trees, support vector machines.