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Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
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Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data.

Akihiro Kitani1, Yusuke Matsui1,2

  • 1Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan.

Biorxiv : the Preprint Server for Biology
|September 10, 2024
PubMed
Summary

Machine learning models predict cognitive resilience (CR) in Alzheimer's disease (AD) patients. The best model identified key genes, aiding understanding of brain health despite AD pathology.

Keywords:
Alzheimer’s diseaseShapley additive explanationsmachine learningresilience gene analyzertranscriptomics

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Area of Science:

  • Neuroscience
  • Genomics
  • Computational Biology

Background:

  • Alzheimer's disease (AD) is characterized by amyloid plaques and neurofibrillary tangles.
  • Cognitive resilience (CR) describes preserved cognitive function despite AD pathology.
  • Understanding CR mechanisms is crucial for developing effective interventions.

Purpose of the Study:

  • To build and compare machine learning models for predicting CR scores.
  • To identify genes and biological pathways associated with CR using RNA-seq data.
  • To develop a tool for visualizing gene contributions to CR.

Main Methods:

  • Utilized RNA-sequencing data from the ROSMAP and MSBB cohorts.
  • Evaluated various machine learning models including SVR, random forest, XGBoost, linear, and transformer-based approaches.
  • Employed Shapley additive explanations (SHAP) to identify important genes.

Main Results:

  • The Support Vector Regression (SVR) model demonstrated superior performance in predicting CR scores.
  • SHAP analysis identified specific genes contributing to cognitive resilience.
  • Biological pathways linked to CR were elucidated through gene analysis.

Conclusions:

  • Machine learning, particularly SVR, can effectively predict cognitive resilience in Alzheimer's disease.
  • Gene expression patterns offer insights into the biological underpinnings of cognitive resilience.
  • The developed Resilience Gene Analyzer (REGA) tool facilitates the interpretation of gene contributions to CR.