Computational approaches for understanding the diagnosis and treatment of Parkinson's disease
| Autor: | Jane E. Alty, Amy Cording, Richard Maguire, Diana Ivanoiu, Christopher J. H. Elliott, Camille Lyle, Michael A. Lones, Mary Elizabeth Pownall, Jeremy Cosgrove, Matthew Bedder, Stephen L. Smith |
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| Rok vydání: | 2015 |
| Předmět: |
Male
Parkinson's disease Remote patient monitoring Evolutionary algorithm Disease Biology Machine learning computer.software_genre Motor function Article Antiparkinson Agents 03 medical and health sciences 0302 clinical medicine Genetics medicine Animals Humans Diagnosis Computer-Assisted Molecular Biology Zebrafish 030304 developmental biology 0303 health sciences business.industry fungi food and beverages Parkinson Disease Cell Biology Disease monitoring medicine.disease 3. Good health Clinical Practice Patient diagnosis Drosophila melanogaster Modeling and Simulation Female Artificial intelligence business computer Algorithms 030217 neurology & neurosurgery Biotechnology |
| Zdroj: | IET Systems Biology. 9:226-233 |
| ISSN: | 1751-8857 1751-8849 |
| DOI: | 10.1049/iet-syb.2015.0030 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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