Computational approaches for understanding the diagnosis and treatment of Parkinson's disease. Issue 6 (1st December 2015)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: IET systems biology. Volume 9:Issue 6(2015)
• Page Start: 226
• Page End: 233
• Publication Date: 2015-12-01
• Subjects: diseases -- patient diagnosis -- patient treatment -- genetic algorithms -- sensors -- patient monitoring -- medical diagnostic computing -- drugs; computational approaches -- Parkinson disease diagnosis -- Parkinson disease treatment -- evolutionary algorithms -- motor function -- animal models -- sensors -- noninvasive procedures -- human data classification -- disease monitoring -- fruit flies -- genetic mutations -- proboscis extension reflex -- drug therapy; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2015.0030
• Languages: English
• ISSNs: 1751-8849

Abstract

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.