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Related Concept Videos

Parkinson's Disease: Overview01:15

Parkinson's Disease: Overview

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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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Parkinson's Disease: Treatment01:24

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Neurodegenerative disorders, such as Parkinson's Disease (PD), involve the gradual and irreversible destruction of neurons in particular brain areas. These disorders exhibit standard features like proteinopathies, selective vulnerability of some neurons, and an interaction of intrinsic properties, genetics, and environmental influences in neural injury.
Parkinson's Disease is primarily a result of the loss of dopaminergic neurons in the substantia nigra pars compacta. The cornerstone of...
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Related Experiment Video

Updated: Sep 21, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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A Machine Learning Approach to Parkinson's Disease Blood Transcriptomics.

Ester Pantaleo1,2,3, Alfonso Monaco1, Nicola Amoroso1,4

  • 1Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy.

Genes
|May 28, 2022
PubMed
Summary

Blood transcriptomics shows promise for early Parkinson

Keywords:
Parkinson’s diseaseblood transcriptomicsfeature selectioninflammationmachine learningmitochondrial dysfunctionoxidative stressxgboost

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Area of Science:

  • Biomarkers
  • Neurodegenerative Diseases
  • Genomics

Background:

  • Parkinson's disease (PD) presents a significant health burden with delayed diagnosis.
  • Current diagnostic methods often identify PD after irreversible neurological damage has occurred.
  • Effective early diagnostic tools are crucial for timely intervention and management.

Purpose of the Study:

  • To evaluate blood transcriptomics for early Parkinson's disease (PD) diagnosis.
  • To identify candidate genes associated with PD using machine learning.
  • To explore the functional significance of identified genes in PD pathogenesis.

Main Methods:

  • Utilized transcriptome data from approximately 550 early PD subjects and healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI) study.
  • Employed a nested feature selection procedure utilizing Random Forests and XGBoost algorithms for classification.
  • Conducted functional analysis using Gene Ontology (GO) and KEGG pathways to interpret candidate genes.

Main Results:

  • Achieved an Area Under the Curve (AUC) of 72% in classifying PD versus HC.
  • Identified 493 candidate genes potentially associated with early Parkinson's disease.
  • Functional analysis provided insights into the biological pathways implicated by these candidate genes.

Conclusions:

  • Blood transcriptomics is a promising approach for the early detection of Parkinson's disease.
  • The identified 493 candidate genes offer potential biomarkers for PD diagnosis.
  • Further research into these genes could elucidate PD mechanisms and inform therapeutic strategies.