Open AccessComputational approaches for understanding the diagnosis and treatment of Parkinson's disease
Author(s) -
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.
Publication year - 2015
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2015.0030
Subject(s) - parkinson's disease , disease , human disease , computer science , range (aeronautics) , machine learning , artificial intelligence , medicine , engineering , pathology , aerospace engineering
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.