scholarly journals Classification of Parkinson’s Disease Genotypes in Drosophila Using Spatiotemporal Profiling of Vision

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Ryan J.H. West ◽  
Christopher J.H. Elliott ◽  
Alex R. Wade
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals 3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

2021 ◽  
Author(s):  
Nikhil J. Dhinagar ◽  
Sophia I. Thomopoulos ◽  
Conor Owens-Walton ◽  
Dimitris Stripelis ◽  
Jose Luis Ambite ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Brain Mri

scholarly journals The Landscape of Parkin Variants Reveals Pathogenic Mechanisms and Therapeutic Targets in Parkinson’s Disease

2018 ◽  
Author(s):  
Wei Yi ◽  
Emma J. MacDougall ◽  
Matthew Y. Tang ◽  
Andrea I. Krahn ◽  
Ziv Gan-Or ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ubiquitin Ligase ◽  
E3 Ubiquitin Ligase ◽  
Specific Drug ◽  
Common Cause ◽  
Naturally Occurring ◽  
Population Databases ◽  
Early Onset Parkinson’S Disease

AbstractMutations in Parkin (PARK2), which encodes an E3 ubiquitin ligase implicated in mitophagy, are the most common cause of early onset Parkinson’s Disease (PD). Hundreds of naturally occurring Parkin variants have been reported, both in PD patient and population databases. However, the effects of the majority of these variants on the function of Parkin and in PD pathogenesis remains unknown. Here we develop a framework for classification of the pathogenicity of Parkin variants based on the integration of clinical and functional evidence – including measures of mitophagy and protein stability, and predictive structural modeling – and assess 51 naturally occurring Parkin variants accordingly. Surprisingly, only a minority of Parkin variants, even among those previously associated with PD, disrupted Parkin function. Moreover, a few of these naturally occurring Parkin variants actually enhanced mitophagy. Interestingly, impaired mitophagy in several of the most common pathogenic Parkin variants could be rescued both by naturally-occurring (p.V224A) and structure-guided designer (p.W403A; p.F146A) hyperactive Parkin variants. Together, the findings provide a coherent framework to classify Parkin variants based on pathogenicity and suggest that several pathogenic Parkin variants represent promising targets to stratify patients for genotype-specific drug design.

IMU-Based Classification of Parkinson's Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection

2018 ◽  
Vol 22 (6) ◽  
pp. 1765-1774 ◽  
Author(s):  
Carlotta Caramia ◽  
Diego Torricelli ◽  
Maurizio Schmid ◽  
Adriana Munoz-Gonzalez ◽  
Jose Gonzalez-Vargas ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Sensitivity Analysis ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Sensor Location

Comparison of Feature Selection in The Classification Of Patients With Parkinson's Disease

2021 ◽  
Author(s):  
Sitti Harlina ◽  
Adhy Rizaldy ◽  
Usman ◽  
Magfirah ◽  
Asran ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

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