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Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

Citation

Smith, SL and Lones, MA and Bedder, M and Alty, JE and Cosgrone, J and Maguire, RJ and Pownall, ME and Ivanoui, D and Lyle, C and Cording, A and Elliott, CJH, Computational approaches for understanding the diagnosis and treatment of Parkinson's disease, IET Systems Biology, 9, (6) pp. 226-233. ISSN 1751-8849 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 The Institution of Engineering and Technology

DOI: doi:10.1049/iet-syb.2015.0030

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.

Item Details

Item Type:Refereed Article
Keywords:parkinson's, artificial intelligence, animal models, diagnostics
Research Division:Biomedical and Clinical Sciences
Research Group:Neurosciences
Research Field:Neurology and neuromuscular diseases
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Alty, JE (Associate Professor Jane Alty)
ID Code:144188
Year Published:2015
Web of Science® Times Cited:10
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2021-04-27
Last Modified:2021-06-30
Downloads:0

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