Browse by author
Lookup NU author(s): Mark Hewitt,
Dr Simon Wilkinson
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Predicting the rate of percutaneous absorption across mammalian and artificial membranes is a complex problem. In previous studies, prediction and accuracy are approached using different machine learning models. Results show that Gaussian processes provided the best result, based on a range of statistical measures. In general the ultimate aim of these machine learning experiments is to try to understand, analyze and predict the percutaneous absorption of drugs across human skin. One way to do this is to select the best set of chemical descriptors and the dataset of synthetic (Polydimethylsiloxane, PDMS) membranes, containing so many descriptors, is considered a suitable dataset to use in this study. Hence, one of the main purposes of the study is to use feature selection methods to select the molecular properties that exert the most important influence on percutaneous absorption across PDMS membranes, in the hope that this will better inform studies on human skin.
Author(s): Binjumah WM, Sun Y, Hewitt M, Adams R, Davey N, Gullick DR, Wilkinson SC, Cronin M, Moss GP
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI)
Year of Conference: 2014
Online publication date: 15/12/2014
Acceptance date: 01/01/1900
Library holdings: Search Newcastle University Library for this item