ArticleBrain communications2022
Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease.
Article in Brain communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Pooled it
- AI-based characterization of Alzheimer's disease phenotypes from population-scale single-cell data.Nature medicine · 2026Article
- Robust transcriptomic signatures of Alzheimer's disease progression: validated explainable AI approach.Scientific reports · 2026Article
- Transcriptomic Insights Into Alzheimer's Disease: Differentially Expressed Genes and Cholesterol Metabolism.CNS neuroscience & therapeutics · 2026Article
- Alzheimer's disease diagnosis based on gene selection and cross-tissue validation.Frontiers in genetics · 2026Article
- GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Celebrating neuropathology's contributions to Alzheimer's Disease Research Centers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Article
- scAGG: Sample-level embedding and classification of Alzheimer's disease from single-nucleus data.Computational and structural biotechnology journal · 2025Article
- scHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering.Briefings in bioinformatics · 2024Article
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Deep Learning-Based Diagnosis of Alzheimer's Disease.Journal of personalized medicine · 2022Review
- The Key Factors Predicting Dementia in Individuals With Alzheimer's Disease-Type Pathology.Frontiers in aging neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.