dc.contributor.author | Sun, Yi | |
dc.contributor.author | Moss, Gary | |
dc.contributor.author | Prapopoulou, M. | |
dc.contributor.author | Adams, Roderick | |
dc.contributor.author | Brown, Marc | |
dc.contributor.author | Davey, N. | |
dc.contributor.editor | Gunopulos, D | |
dc.contributor.editor | Turini, F | |
dc.contributor.editor | Zaniolo, C | |
dc.contributor.editor | Ramakrishnan, N | |
dc.contributor.editor | Wu, XD | |
dc.date.accessioned | 2016-03-03T12:24:37Z | |
dc.date.available | 2016-03-03T12:24:37Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Sun , Y , Moss , G , Prapopoulou , M , Adams , R , Brown , M & Davey , N 2008 , Prediction of skin penetration using machine learning methods . in D Gunopulos , F Turini , C Zaniolo , N Ramakrishnan & XD Wu (eds) , Procs of the 8th IEEE International Conference on Data Mining : (ICDM'08) . Institute of Electrical and Electronics Engineers (IEEE) , pp. 1049-1054 , 8th IEEE International Conference on Data Mining , Pisa , 15/12/08 . https://doi.org/10.1109/ICDM.2008.97 | |
dc.identifier.citation | conference | |
dc.identifier.isbn | 978-0-7695-3502-9 | |
dc.identifier.uri | http://hdl.handle.net/2299/16667 | |
dc.description.abstract | Improving predictions of the skin permeability coefficient is a difficult problem. It is also an important issue with the increasing use of skin patches as a means of drug delivery. In this work, we applyK-nearest-neighbour regression, single layer networks, mixture of experts and Gaussian processes to predict the permeability coefficient. We obtain a considerable improvement over the quantitative structureactivity relationship (QSARs) predictors. We show that using five features, which are molecular weight, solubility parameter, lipophilicity, the number of hydrogen bonding acceptor and donor groups, can produce better predictions than the one using only lipophilicity and the molecular weight. The Gaussian process regression with five compound features gives the best performance in this work. | en |
dc.format.extent | 6 | |
dc.format.extent | 261976 | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.ispartof | Procs of the 8th IEEE International Conference on Data Mining | |
dc.subject | STRUCTURE-PERMEABILITY RELATIONSHIPS | |
dc.subject | PERCUTANEOUS-ABSORPTION | |
dc.title | Prediction of skin penetration using machine learning methods | en |
dc.contributor.institution | Science & Technology Research Institute | |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Health & Human Sciences Research Institute | |
dc.contributor.institution | Department of Pharmacy | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=67049097965&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1109/ICDM.2008.97 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |