Smith, S.L., Lones, M.A., Bedder, M. et al. (8 more authors) (2015) Computational approaches for understanding the diagnosis and treatment of Parkinson's disease. IET Systems Biology, 9 (6). pp. 226-233.
Abstract
This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Institution of Engineering and Technology. This is an author produced version of a paper subsequently published in IET Systems Biology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | sensors; diseases; drugs; genetic algorithms; medical diagnostic computing; patient diagnosis; patient monitoring; patient treatment. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Infection, Immunity and Cardiovascular Disease |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Feb 2017 13:13 |
Last Modified: | 29 Mar 2018 08:11 |
Published Version: | https://doi.org/10.1049/iet-syb.2015.0030 |
Status: | Published |
Publisher: | Institution of Engineering and Technology |
Identification Number: | 10.1049/iet-syb.2015.0030 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110792 |