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February 26, 2026Brain SciencesOpen Access

A Two-Stage Framework for Early Detection and Subtype Identification of Alzheimer’s Disease Through Multimodal Biomarker Extraction and Improved GCN

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Authors

JLJunshuai LiAnyang Normal University WKWei KongBeijing University of Chemical Technology SWShuaiqun WangShanghai Maritime University

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Implication

Implements a two-stage framework to enhance early detection and subtype identification of Alzheimer’s Disease, indicating new avenues for precision medicine.

Key Points

The study aims to develop a multimodal framework for early detection and subtype identification of Alzheimer’s disease using advanced imaging and transcriptomic analysis.
Developed two-stage multimodal feature extraction framework (MFEAA-GCNSASE) for identifying biomarkers.
Integrated structural MRI, PET, and transcriptomic data for association analysis.
Utilized self-attention and self-expression layers in GCNSASE for enhanced classification accuracy.
Conducted unsupervised clustering on MCI samples to explore subtype heterogeneity.
Identified robust biomarkers including Left Hippocampus and key genes SLC25A5 and GABARAP.
Achieved high discrimination performance with AUC values between 0.946-0.961 across feature subsets.
Revealed two distinct MCI subtypes with different molecular landscapes and conversion risks.
Majority of MCI patients converting to AD were identified in the neuroinflammatory subtype.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/699fe3ec95ddcd3a253e7f0b https://doi.org/10.3390/brainsci16030255

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February 26, 2026Brain SciencesOpen Access

A Two-Stage Framework for Early Detection and Subtype Identification of Alzheimer’s Disease Through Multimodal Biomarker Extraction and Improved GCN

View Full Paper

Ask AI

Bookmark

Authors

JLJunshuai LiAnyang Normal University WKWei KongBeijing University of Chemical Technology SWShuaiqun WangShanghai Maritime University

Discussion

Member takes

Implication

Implements a two-stage framework to enhance early detection and subtype identification of Alzheimer’s Disease, indicating new avenues for precision medicine.

Key Points

The study aims to develop a multimodal framework for early detection and subtype identification of Alzheimer’s disease using advanced imaging and transcriptomic analysis.
Developed two-stage multimodal feature extraction framework (MFEAA-GCNSASE) for identifying biomarkers.
Integrated structural MRI, PET, and transcriptomic data for association analysis.
Utilized self-attention and self-expression layers in GCNSASE for enhanced classification accuracy.
Conducted unsupervised clustering on MCI samples to explore subtype heterogeneity.
Identified robust biomarkers including Left Hippocampus and key genes SLC25A5 and GABARAP.
Achieved high discrimination performance with AUC values between 0.946-0.961 across feature subsets.
Revealed two distinct MCI subtypes with different molecular landscapes and conversion risks.
Majority of MCI patients converting to AD were identified in the neuroinflammatory subtype.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/699fe3ec95ddcd3a253e7f0b https://doi.org/10.3390/brainsci16030255

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