The application of Gaussian processes in the predictions of permeability across mammalian membranes

Yi Sun, Marc B. Brown, Maria Prapopoulou, Rod Adams, Neil Davey, Gary P. Moss

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The problem of predicting the rate of percutaneous absorption of a drug is an important issue with the increasing use of the skin as a means of moderating and controlling drug delivery. The aim of the current study was to explore whether including another species skin data in a training set can improve predictions of the human skin permeability coefficient. Permeability data for absorption across rodent skin was collected from the literature. The Gaussian process model was applied to the data, and this was compared to two QSPR methods. The results demonstrate that data from non-human skin can provide useful information in the prediction of the permeability of human skin.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Pages507-514
Number of pages8
Volume7553 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2012
Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
Duration: 11 Sep 201214 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7553 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other22nd International Conference on Artificial Neural Networks, ICANN 2012
CountrySwitzerland
CityLausanne
Period11/09/1214/09/12

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  • Cite this

    Sun, Y., Brown, M. B., Prapopoulou, M., Adams, R., Davey, N., & Moss, G. P. (2012). The application of Gaussian processes in the predictions of permeability across mammalian membranes. In Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings (PART 2 ed., Vol. 7553 LNCS, pp. 507-514). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7553 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-33266-1_63