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

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,...

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Detalles Bibliográficos
Autores principales: Lai, Yongxing, Lin, Peiqiang, Lin, Fan, Chen, Manli, Lin, Chunjin, Lin, Xing, Wu, Lijuan, Zheng, Mouwei, Chen, Jianhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Immunology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773397/
https://www.ncbi.nlm.nih.gov/pubmed/36569892
http://dx.doi.org/10.3389/fimmu.2022.1046410

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773397/
https://www.ncbi.nlm.nih.gov/pubmed/36569892
http://dx.doi.org/10.3389/fimmu.2022.1046410

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