Smith Stephen L, Lones Michael A, Bedder Matthew, Alty Jane E, Cosgrove Jeremy, Maguire Richard J, Pownall Mary Elizabeth, Ivanoiu Diana, Lyle Camille, Cording Amy, Elliott Christopher J H
Department of Electronics, University of York, Heslington, York Y010 5DD.
School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS.
IET Syst Biol. 2015 Dec;9(6):226-33. doi: 10.1049/iet-syb.2015.0030.
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.
本研究描述了如何应用进化算法（EA）来研究帕金森病（PD）患者及PD动物模型的运动功能。通过一系列遵循传统临床实践的非侵入性程序，利用市售传感器获取人体数据。然后，EA可用于对人体数据进行分类，以用于包括诊断和疾病监测在内的一系列用途。本文给出了新的研究结果，这些结果表明EA还可通过测量果蝇的喙伸展反射，对有无导致帕金森病基因突变的果蝇进行分类。文中提出了一种可应用于PD人体和动物研究的计算方法，为以真正客观的方式评估现有和新的药物疗法奠定了基础。
