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dc.contributor.authorMoss, Gary
dc.contributor.authorShah, A.J.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.contributor.authorSun, Y.
dc.contributor.authorWilkinson, S.C.
dc.contributor.authorPugh, W.J.
dc.identifier.citationMoss , G , Shah , A J , Adams , R G , Davey , N , Sun , Y , Wilkinson , S C & Pugh , W J 2012 , ' The application of discriminant analysis and Machine Learning methods as tools to identify and classify compounds with potential as transdermal enhancers ' , European Journal of Pharmaceutical Sciences , vol. 45 , no. 1-2 , pp. 116-127 .
dc.identifier.otherPURE: 533721
dc.identifier.otherPURE UUID: 7e7dc7f8-3beb-4308-ac3d-989e9e95f569
dc.identifier.otherScopus: 83655201097
dc.descriptionCopyright 2011 Elsevier B.V., All rights reserved.
dc.description.abstractDiscriminant analysis (DA) has previously been shown to allow the proposal of simple guidelines for the classification of 73 chemical enhancers of percutaneous absorption. Pugh et al. employed DA to classify such enhancers into simple categories, based on the physicochemical properties of the enhancer molecules (Pugh et al., 2005). While this approach provided a reasonable accuracy of classification it was unable to provide a consistently reliable estimate of enhancement ratio (ER, defined as the amount of hydrocortisone transferred after 24 h, relative to control). Machine Learning methods, including Gaussian process (GP) regression, have recently been employed in the prediction of percutaneous absorption of exogenous chemicals (Moss et al., 2009; Lam et al., 2010; Sun et al., 2011). They have shown that they provide more accurate predictions of these phenomena. In this study several Machine Learning methods, including the K-nearest-neighbour (KNN) regression, single layer networks, radial basis function networks and the SVM classifier were applied to an enhancer dataset reported previously. The SMOTE sampling method was used to oversample chemical compounds with ER > 10 in each training set in order to improve estimation of GP and KNN. Results show that models using five physicochemical descriptors exhibit better performance than those with three features. The best classification result was obtained by using the SVM method without dealing with imbalanced data. Following over-sampling, GP gives the best result. It correctly assigned 8 of the 12 "good" (ER > 10) enhancers and 56 of the 59 "poor" enhancers (ER <10). Overall success rates were similar. However, the pharmaceutical advantages of the Machine Learning methods are that they can provide more accurate classification of enhancer type with fewer false-positive results and that, unlike discriminant analysis, they are able to make predictions of enhancer ability.en
dc.relation.ispartofEuropean Journal of Pharmaceutical Sciences
dc.titleThe application of discriminant analysis and Machine Learning methods as tools to identify and classify compounds with potential as transdermal enhancersen
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Pharmacy
dc.contributor.institutionBiocomputation Research Group
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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