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Related Concept Videos

Alzheimer's Disease: Overview01:26

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

Fang Wang1, Ying Liang2, Qin-Wen Wang3

  • 1Department of Pharmacy, Zhejiang Pharmaceutical University, Ningbo, China.

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|December 27, 2024
PubMed
Summary

This study identifies novel Alzheimer's disease (AD) biomarkers, MYH9 and RHOQ, using bioinformatics and machine learning. MYH9 shows promise for improved AD diagnosis and personalized treatment strategies.

Keywords:
Alzheimer’s diseaseBiomarkerHub genes

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Area of Science:

  • Bioinformatics
  • Neurodegenerative Disorders
  • Machine Learning

Background:

  • Alzheimer's disease (AD) lacks effective treatments, necessitating early detection for improved outcomes.
  • Current diagnostic approaches require enhancement for timely intervention.

Purpose of the Study:

  • To identify reliable biomarkers for Alzheimer's disease (AD) diagnosis and treatment.
  • To develop interpretable machine learning models for AD detection.

Main Methods:

  • Differential gene expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and Protein-Protein Interaction (PPI) network construction.
  • Application of machine learning algorithms and SHapley Additive exPlanations (SHAP) for model interpretation.
  • Experimental validation of identified hub genes (MYH9, RHOQ) in an AD cell model and single-cell transcriptome analysis.

Main Results:

  • Identified 10 hub genes associated with AD, including MYH9 and RHOQ.
  • MYH9 demonstrated significant differential mRNA expression in an AD cell model, indicating its potential as a biomarker.
  • RHOQ also showed differential expression, though with opposing patterns in cell models versus human brain tissue, warranting further investigation.

Conclusions:

  • MYH9 is a promising novel biomarker for enhanced Alzheimer's disease (AD) diagnosis and personalized therapy.
  • Interpretable machine learning models offer new insights for AD biomarker discovery.
  • Further research into RHOQ's complex role in AD is recommended.