An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer’s disease

An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer’s disease

Alzheimer’s disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ageing research reviews 2022-11, Vol.81, p.101721-101721, Article 101721
Hauptverfasser: Shokhirev, Maxim N., Johnson, Adiv A.
Format: Artikel
Sprache:eng
Schlagworte:
Age Factors
Aged
Aged, 80 and over
Aging
Alzheimer Disease - diagnosis
Alzheimer Disease - genetics
Alzheimer Disease - metabolism
Alzheimer’s disease
Bioinformatics
Brain - metabolism
Ensemble learning
Humans
Machine Learning
MicroRNAs - genetics
MicroRNAs - metabolism
Omics
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Alzheimer’s disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups:
ISSN:1568-1637
1872-9649
DOI:10.1016/j.arr.2022.101721