Article

BrainBridge Characterizes Key Factors affecting Alzheimer’s Disease and Associated Phenotypes

2025-12-06

Abstract excerpt

Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of Alzheimer’s disease and aging by revealing cellular heterogeneity and shifts in cell-type composition between diseased/old and healthy/young individuals. However, few existing studies utilize the rich information in single-cell transcriptomic atlases for robust patient-level modeling and biological feature selection. To address...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 1350646f-47ea-519e-97b2-5c08288217a3
DOI: 10.64898/2025.12.03.692255

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

BrainBridge Characterizes Key Factors affecting Alzheimer’s Disease and Associated PhenotypesDOI 10.64898/2025.12.03.692255Read

Single-cell machine learning uncovers genetically anchored, cell-type specific programs of Alzheimer’s diseaseSemantic proximity Read

DeepDynamics resolves cell-subtype and clinicopathological dynamics from bulk RNA-seq to identify mediators of Alzheimer’s disease riskSemantic proximity Read

Multiscale transcriptomic organization of the human brain with DigitalBrainSemantic proximity Read

Single-cell atlas of transcriptomic vulnerability across multiple neurodegenerative and neuropsychiatric diseasesSemantic proximity Read

Cell-type-aware transcriptome-wide association studies identify 91 independent risk genes for Alzheimer's disease dementia.Semantic proximity Read

Decoding cell-type-specific alterations in Alzheimer’s disease through scRNA-seq and network analysisSemantic proximity Read

Isolating structured salient variations in single-cell transcriptomic data with StrastiveVISemantic proximity Read

scAGG: Sample-level embedding and classification of Alzheimer’s disease from single-nucleus dataSemantic proximity Read

A Pathway-Based Machine Learning Approach Identifies Region-Specific Markers and Patterns in Alzheimer’s Disease Patients Based on Spatial and Severity MetadataSemantic proximity Read

Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer’s diseaseSemantic proximity Read

Personalized Single-cell Transcriptomics Reveals Molecular Diversity in Alzheimer’s DiseaseSemantic proximity Read

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)Semantic proximity Read