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Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity

Webb, SJ, Hanser, T, Howlin, B, Krause, P and Vessey, JD (2014) Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity JOURNAL OF CHEMINFORMATICS, 6, ARTN 8.

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Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemistry
Authors :
NameEmailORCID
Webb, SJ
Hanser, T
Howlin, B
Krause, P
Vessey, JD
Date : 25 March 2014
Identification Number : 10.1186/1758-2946-6-8
Uncontrolled Keywords : Science & Technology, Physical Sciences, Technology, Chemistry, Multidisciplinary, Computer Science, Information Systems, Computer Science, Interdisciplinary Applications, Chemistry, Computer Science, CHEMISTRY, MULTIDISCIPLINARY, COMPUTER SCIENCE, INFORMATION SYSTEMS, COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS, Interpretation, Interpretable, Machine learning, Mutagenicity, QSAR, QSAR MODELS, PREDICTION, EXTRACTION, CLASSIFICATION, VISUALIZATION
Related URLs :
Additional Information : © 2014 Webb et al.; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Depositing User : Symplectic Elements
Date Deposited : 19 Sep 2014 16:54
Last Modified : 31 Oct 2017 16:53
URI: http://epubs.surrey.ac.uk/id/eprint/805913

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