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

Stephen L Smith, Michael A Lones, Matthew Bedder, Jane E Alty, Jeremy Cosgrove, Richard J Maguire, Mary Elizabeth Pownall, Diana Ivanoiu, Camille Lyle, Amy Cording, Christopher J H Elliott

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)226-233
Number of pages8
JournalIET Systems Biology
Issue number6
Early online date21 Aug 2015
Publication statusPublished - Dec 2015


  • Parkinson disease diagnosis
  • Parkinson disease treatment
  • animal models
  • computational approaches
  • disease monitoring
  • drug therapy
  • evolutionary algorithms
  • fruit flies
  • genetic mutations
  • human data classification
  • motor function
  • noninvasive procedures
  • proboscis extension reflex
  • sensors

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