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dc.contributor.authorAshrafi, P.
dc.contributor.authorMoss, G. P.
dc.contributor.authorWilkinson, S. C.
dc.contributor.authorDavey, N.
dc.contributor.authorSun, Yi
dc.identifier.citationAshrafi , P , Moss , G P , Wilkinson , S C , Davey , N & Sun , Y 2015 , ' The application of machine learning to the modelling of percutaneous absorption: An overview and guide ' , SAR and QSAR in Environmental Research , vol. 26 , no. 3 , pp. 181-204 .
dc.identifier.otherPURE: 9618218
dc.identifier.otherPURE UUID: 99565f80-a1ad-4bee-9091-3abac0212105
dc.identifier.otherScopus: 84925141108
dc.description.abstractMachine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specifically, ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. However, they are beset by perceptions of a lack of transparency and, often, once a ML or related method has been published there is little impetus from other researchers to adopt such methods. This is usually due to the lack of transparency in some methods and the lack of availability of specific coding for running advanced ML methods. This paper reviews critically the application of ML methods to percutaneous absorption and addresses the key issue of transparency by describing in detail – and providing the detailed coding for – the process of running a ML method (in this case, a Gaussian process regression method). Although this method is applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.en
dc.relation.ispartofSAR and QSAR in Environmental Research
dc.subjectGaussian process
dc.subjectmachine learning
dc.subjectpercutaneous absorption
dc.subjectquantitative structure–permeability relationships (QSPRs)
dc.subjectskin permeation
dc.titleThe application of machine learning to the modelling of percutaneous absorption: An overview and guideen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionDepartment of Pharmacy
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Life and Medical Sciences
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Life and Medical Sciences
rioxxterms.typeJournal Article/Review

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