Identification of blood-based transcriptomics biomarkers for Alzheimer's disease using statistical and machine learning classifier-Reference-Cited by-同舟云学术

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Identification of blood-based transcriptomics biomarkers for Alzheimer's disease using statistical and machine learning classifier

Published:2022 Issue: Volume:33 Page:101083
ISSN:2352-9148
Container-title:Informatics in Medicine Unlocked
language:en
Short-container-title:Informatics in Medicine Unlocked

Author:

Abdullah Mohammad Nasir^ORCID,Wah Yap Bee,Abdul Majeed Abu Bakar,Zakaria Yuslina,Shaadan Norshahida

Funder

UiTM Institute of Research Management & Innovation

Publisher

Elsevier BV

Subject

Health Informatics

Reference65 articles.

1. Transcriptomics in alzheimer's disease: aspects and challenges;Bagyinszky;Int J Mol Sci,2020

2. Sex differences in the incidence and prevalence of young-onset Alzheimer's disease: a meta-analysis;Peeters;Int J Geriatr Psychiatr,2022

3. Plaque and tangle imaging and cognition in normal aging and Alzheimer's disease;Braskie;Neurobiol Aging,2010

4. Dementia: a public health priority;World Health Organization,2012

5. The epidemiology of Alzheimer's disease modifiable risk factors and prevention;Zhang;J Prev Alzheimer’s Dis,2021

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1. Targeted co-expression networks for the study of traits;Scientific Reports;2024-07-19

2. Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia;Alzheimer's Research & Therapy;2024-06-01

3. Towards early diagnosis of Alzheimer’s disease: advances in immune-related blood biomarkers and computational approaches;Frontiers in Immunology;2024-04-23

4. Genome-wide epistasis analysis reveals gene–gene interaction network on an intermediate endophenotype P-tau/Aβ42 ratio in ADNI cohort;Scientific Reports;2024-02-17

5. Identifying potential genetic epistasis implicated in Alzheimer’s disease via detection of SNP-SNP interaction on quantitative trait CSF Aβ42;Neurobiology of Aging;2024-02

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