Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease.

Authors

Yuxin Yang, Jielin Xu, Yuan Hou, Yadi Zhou, Andrew J Saykin, Feixiong Cheng

Published in

Alzheimer's & dementia (Amsterdam, Netherlands). Volume 18. Issue 3. Pages e70406. Epub Jun 29, 2026.

Abstract

Identifying disease-modifying drug targets is crucial for developing effective Alzheimer's disease (AD) treatments.
We present a deep contrastive learning framework for cell type-specific AD-associated genes identification (alzCL). alzCL creates cell-type-specific representations of genes by integrating human brain single-nucleus RNA-sequencing data with the human protein-protein interactome, thereby capturing both genetic signatures and functional features.
By integrating human brain snRNA-seq data, alzCL outperforms the state-of-the-art models by 18% to 24% in area under the receiver operating characteristic curve. Via alzCL, we computationally identified 16, 164, and 221 AD-associated genes across astrocytes, microglia, and inhibitory neurons, respectively. Top prioritized genes (e.g., MAP3K5, P2RX4, and PRKD1) are significantly enriched in multiple AD-associated inflammatory and other pathobiological pathways. By integrating drug-target interaction data with alzCL-predicted AD-associated genes, we identified potential repurposable drugs for AD, including selonsertib and paroxetine.
AlzCL offers a deep contrastive learning framework for discovery of disease-associated genes and drug targets in AD.

PMID:
42382038
Bibliographic data and abstract were imported from PubMed on 13 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.