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dc.contributor.authorSun, Yi.
dc.contributor.authorLam, L.Y.
dc.contributor.authorMoss, G.P.
dc.contributor.authorPrapopoulou, M.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.contributor.authorGray, David
dc.contributor.authorBrown, Marc
dc.date.accessioned2011-04-20T14:02:22Z
dc.date.available2011-04-20T14:02:22Z
dc.date.issued2010
dc.identifier.citationSun , Y , Lam , L Y , Moss , G P , Prapopoulou , M , Adams , R G , Davey , N , Gray , D & Brown , M 2010 , Predicting drug absorption rates through human skin . in IEEE International Joint Conference on Neural Networks (IJCNN) No. 11593964 . IEEE , pp. 1-5 . https://doi.org/10.1109/IJCNN.2010.5596603
dc.identifier.isbn978-1-4244-6916-1
dc.identifier.otherPURE: 185388
dc.identifier.otherPURE UUID: 4de16278-5916-4d12-a069-7eb79fc1e780
dc.identifier.otherdspace: 2299/5703
dc.identifier.otherScopus: 79959420620
dc.identifier.urihttp://hdl.handle.net/2299/5703
dc.description“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”
dc.description.abstractPredicting the rate at which a substance will pass through human skin and into the bloodstream is a problem of current interest. We use Gaussian Process modeling to train a set of predictors using every combination of six molecular features. We find that only three of the features are needed for our best predictor. This result could be useful in the further analysis of skin permeability.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIEEE International Joint Conference on Neural Networks (IJCNN) No. 11593964
dc.rights/dk/atira/pure/core/openaccesspermission/open
dc.subjectstructure-permeability relationships
dc.subjectpercutaneous-absorption
dc.titlePredicting drug absorption rates through human skinen
dc.contributor.institutionDepartment of Pharmacy
dc.contributor.institutionHealth & Human Sciences Research Institute
dc.contributor.institutionSchool of Life and Medical Sciences
dc.relation.schoolSchool of Life and Medical Sciences
rioxxterms.versionofrecordhttps://doi.org/10.1109/IJCNN.2010.5596603
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstyperestrictedAccess


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