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Feature Selection Modelling for Percutaneous Absorption across Synthetic Membranes

Lookup NU author(s): Mark Hewitt, Dr Simon Wilkinson


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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.

Publication metadata

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

Pages: 1021-1025

Online publication date: 15/12/2014

Acceptance date: 01/01/1900

ISSN: 1082-3409

Publisher: IEEE


DOI: 10.1109/ICTAI.2014.155

Library holdings: Search Newcastle University Library for this item

ISBN: 9781479965724