| dc.contributor.author | Sun, Yi. | |
| dc.contributor.author | Lam, L.Y. | |
| dc.contributor.author | Moss, G.P. | |
| dc.contributor.author | Prapopoulou, M. | |
| dc.contributor.author | Adams, R.G. | |
| dc.contributor.author | Davey, N. | |
| dc.contributor.author | Gray, David | |
| dc.contributor.author | Brown, Marc | |
| dc.date.accessioned | 2011-04-20T14:02:22Z | |
| dc.date.available | 2011-04-20T14:02:22Z | |
| dc.date.issued | 2010 | |
| dc.identifier.citation | Sun , 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 . Institute of Electrical and Electronics Engineers (IEEE) , pp. 1-5 . https://doi.org/10.1109/IJCNN.2010.5596603 | |
| dc.identifier.isbn | 978-1-4244-6916-1 | |
| dc.identifier.other | dspace: 2299/5703 | |
| dc.identifier.uri | http://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.abstract | Predicting 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.format.extent | 231058 | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
| dc.relation.ispartof | IEEE International Joint Conference on Neural Networks (IJCNN) No. 11593964 | |
| dc.subject | structure-permeability relationships | |
| dc.subject | percutaneous-absorption | |
| dc.title | Predicting drug absorption rates through human skin | en |
| dc.contributor.institution | Department of Pharmacy | |
| dc.contributor.institution | Health & Human Sciences Research Institute | |
| dc.contributor.institution | School of Life and Medical Sciences | |
| rioxxterms.versionofrecord | 10.1109/IJCNN.2010.5596603 | |
| rioxxterms.type | Other | |
| herts.preservation.rarelyaccessed | true | |
