AIMC Topic: Parkinson Disease

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Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.

CNS neuroscience & therapeutics
OBJECTIVE: Current clinical evaluation may tend to lack precision in detecting depression in Parkinson's disease (DPD). Radiomics features have gradually shown potential as auxiliary diagnostic tools in identifying and distinguishing different subtyp...

Identification of CXCR4 inhibitory activity in natural compounds using cheminformatics-guided machine learning algorithms.

Integrative biology : quantitative biosciences from nano to macro
Neurodegenerative disorders are characterised by progressive damage to neurons that leads to cognitive impairment and motor dysfunction. Current treatment options focus only on symptom management and palliative care, without addressing their root cau...

Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning.

PloS one
Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder. The inflammatory response is essential in the pathogenesis and progression of PD. The goal of this study is to combine bioinformatics and machine learning to screen for...

Amphetamine use and Parkinson's disease: integration of artificial intelligence prediction, clinical corroboration, and mechanism of action analyses.

PloS one
Parkinson's disease (PD) is an increasingly prevalent neurologic condition for which symptomatic, but not preventative, treatment is available. Drug repurposing is an innovate drug discovery method that uncovers existing therapeutics to treat or prev...

Deep Learning-Based Prediction of Freezing of Gait in Parkinson's Disease With the Ensemble Channel Selection Approach.

Brain and behavior
PURPOSE: A debilitating and poorly understood symptom of Parkinson's disease (PD) is freezing of gait (FoG), which increases the risk of falling. Clinical evaluations of FoG, relying on patients' subjective reports and manual examinations by speciali...

Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these res...

m6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Medicine
Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on var...

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.

Clinical and translational science
Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techni...

Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Translational vision science & technology
PURPOSE: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia a...

Simulating Accelerometer Signals of Parkinson's Gait Using Generative Adversarial Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Wearable technologies have been demonstrated to have value in the objective assessment of Parkinson's disease. However, certain symptoms such as freezing of gait are challenging to monitor using current approaches. Data augmentation, wherein syntheti...