Show simple item record Yu, P. en Wild, D.J. en 2014-11-04T13:16:23Z en 2014-11-04T13:16:23Z en 2012 en
dc.identifier.citation Yu, P., & Wild, D. J. (2012). Fast rule-based bioactivity prediction using associative classification mining. Journal of Cheminformatics, 4, 29. en
dc.description.abstract Relating chemical features to bioactivities is critical in molecular design and is used extensively in the lead discovery and optimization process. A variety of techniques from statistics, data mining and machine learning have been applied to this process. In this study, we utilize a collection of methods, called associative classification mining (ACM), which are popular in the data mining community, but so far have not been applied widely in cheminformatics. More specifically, classification based on predictive association rules (CPAR), classification based on multiple association rules (CMAR) and classification based on association rules (CBA) are employed on three datasets using various descriptor sets. Experimental evaluations on anti-tuberculosis (antiTB), mutagenicity and hERG (the human Ether-a-go-go-Related Gene) blocker datasets show that these three methods are computationally scalable and appropriate for high speed mining. Additionally, they provide comparable accuracy and efficiency to the commonly used Bayesian and support vector machines (SVM) methods, and produce highly interpretable models. en
dc.language.iso en_US en
dc.publisher Chemistry Central Ltd. en
dc.relation.isversionof en
dc.rights © 2012 Yu and Wild. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en
dc.rights.uri en
dc.subject Associative classification mining en
dc.subject Bayesian en
dc.subject Fingerprint en
dc.subject Pipeline Pilot en
dc.subject SVM en
dc.title Fast rule-based bioactivity prediction using associative classification mining en
dc.type Article en
dc.altmetrics.display true en

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