Elucidating Microglial Heterogeneity and Functions in Alzheimer’s Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction

• Published in: Scientific reports Vol. 14; no. 1; pp. 17271 - 11
• Main Authors: Wu, Xinyi, Liu, Mingyu, Zhang, Xinyue, Pan, Xue, Cui, Xiaotong, Jin, Jiahui, Sun, Huanan, Xiao, Chuyu, Tong, Xiangyi, Ren, Liou, Wang, Yaxuan, Cao, Xuezhao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.07.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/48; 631/378; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer Disease - pathology; Alzheimer's disease; Cell adhesion; Cell interactions; Central nervous system; Convolutional neural network; Deep Learning; Gene Expression Profiling; Gene Regulatory Networks; Heterogeneity; Humanities and Social Sciences; Humans; Microglia; Microglia - metabolism; Microglia - pathology; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Science; Science (multidisciplinary); scRNA-seq; Single-Cell Analysis - methods; Synapses; Therapeutic applications; Therapeutic targets; scRNA-seq; Alzheimer’s disease; Convolutional neural network; Microglia

In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional neural network (CNN) model. Focusing on the...