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

ABSTRACT:

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 SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of

SUBMITTER: Lai Y 

PROVIDER: S-EPMC9773397 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature