Identification of immune microenvironment subtypes and signature genes for Alzheimer’s disease diagnosis and risk prediction based on explainable machine learning

Abstract excerpt

Background: Using interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD. Methods: ssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the...

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