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The application of machine learning to the modelling of percutaneous absorption: An overview and guide

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Machine 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.
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Keywords: Gaussian process; machine learning; percutaneous absorption; quantitative structure–permeability relationships (QSPRs); skin permeation

Document Type: Research Article

Affiliations: 1: School of Computer Science, University of Hertfordshire, Hatfield, UK 2: School of Pharmacy, Keele University, Keele, UK 3: Medical Toxicology Centre, Institute for Cellular Medicine, University of Newcastle-upon-Tyne, UK

Publication date: March 4, 2015

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