Gene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches

Abstract

Despite numerous studies in the field of dementia and Alzheimer's disease (AD), a comprehensive understanding of this devastating disease remains elusive. Bulk transcriptomics have provided insights into the underlying genetic factors at a high level. Subsequent technological advancements have focused on single‐cell omics, encompassing techniques such as single‐cell RNA sequencing and epigenomics, enabling the capture of RNA transcripts and chromatin states at a single cell or nucleus resolution. Furthermore, the emergence of spatial omics has allowed the study of gene responses in the vicinity of amyloid beta plaques or across various brain regions. With the vast amount of data generated, utilizing gene regulatory networks to comprehensively study this disease has become essential. This review delves into some techniques employed in the field of AD, explores the discoveries made using these techniques, and provides insights into the future of the field.

MetaGXplore: Integrating Multi-Omics Data with Graph Convolutional Networks for Pan-cancer Patient Metastasis Identification

“…AI-based predictive models utilizing multiomics data have shown significant potential across various applications. The advancement of high-throughput sequencing technology and the advent of multi-omics sequencing have exponentially increased the molecular characteristics that can be used as features and insights into pathophysiology [11][12][13][14]. However, the inherent heterogeneity of cancer results in diverse molecular characteristics among patients.…”