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Introducing the consensus modeling concept in genetic algorithms: application to interpretable discriminant analysis.

Ganguly, Milan and Brown, Nathan and Schuffenhauer, Ansgar and Ertl, Peter and Gillet, Valerie J. and Greenidge, Paulette (2006) Introducing the consensus modeling concept in genetic algorithms: application to interpretable discriminant analysis. Journal of Chemical Information and Modeling, 46 (5). pp. 2110-2124. ISSN 1549-9596

Abstract

An evolutionary statistical learning method was applied to classify drugs according to their biological target and also to discriminate between a compilation of oral and nonoral drugs. The emphasis was placed not only on how well the models predict but also on their interpretability. In an enhancement to previous studies, the consistency of the model weights over several runs of the genetic algorithm was considered with the goal of producing comprehensible models. Via this approach, the descriptors and their ranges that contribute most to class discrimination were identified. Selecting a bin step size that enables the average descriptor properties of the class being trained to be captured improves the interpretability and discriminatory power of a model. The performance, consistency, and robustness of such models were further enhanced by using two novel approaches that reduce the variability between individual solutions: consensus and splice modeling. Finally, the ability of the genetic algorithm to discriminate between activity classes was compared with a similarity searching method, while naïve Bayes classifiers and support vector machines were applied in discriminating the oral and nonoral drugs.

Item Type: Article
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Date Deposited: 14 Dec 2009 13:57
Last Modified: 31 Jan 2013 01:12
URI: https://oak.novartis.com/id/eprint/591

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