ADInteractome: discovering Alzheimer's disease-associated gene interactions from single-nucleus RNA-seq data through neural interaction states.
7 affiliations
- 1College of Science and Engineering, University of Minnesota Twin Cities, Mayo D528, 420 Delaware St SE, Minneapolis, MN 55455, United States.
- 2McWilliams School of Biomedical Informatics, UTHealth Houston, 7000 Fannin St, Suite 600, Houston, TX 77030, United States.
- 3MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, United States.
- 4Department of Computer Science, University of North Carolina at Charlotte, Woodward Hall, 9201 University City Blvd, Charlotte, NC 28223, United States.
- 5Department of Computer Science & Engineering, University of Minnesota Twin Cities, 4-192 Keller Hall, 200 Union Street SE, Minneapolis, MN 55455, United States.
- 6Department of Computer Science, Rice University, Duncan Hall 3122, 6100 Main Street, Houston, TX 77005, United States.
- 7School of Computing, Stevens Institute of Technology, Gateway Center North, 1 Castle Point Terrace, Hoboken, NJ 07030, United States.
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by amyloid $\beta $ plaques and tau tangles, yet its molecular mechanisms remain elusive. Advances in single-nucleus RNA sequencing (snRNA-seq) have provided profound insights into the cellular heterogeneity of AD-associated brains. In this study, we introduce ADInteractome, an interpretable artificial intelligence framework designed to discover gene interactions associated with AD directly from the internal state of neural networks trained on the snRNA-seq data of brain cells. The method is based on the intuition that if a neural network can accurately classify cells from AD and non-AD donors based on their snRNA-seq data, then its internal interaction states, reflecting interaction patterns within the gene expression data, are informative for identifying biologically meaningful gene interactions. Applied to a large-scale snRNA-seq dataset from the Seattle Alzheimer's Disease Brain Cell Atlas, ADInteractome reveals cell-type-specific and AD-associated gene circuits supported by existing literature. The results were further supported by our virtual knock-out and knock-in experiments, a novel concept to probe our findings through a data-driven approach. Compared with traditional co-expression network methods, it achieves stronger enrichment for known AD-associated gene interactions and uncovers novel interaction patterns. These findings highlight the potential of interpretable AI approaches for elucidating complex molecular interactions in neurodegenerative disease.
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BibTeX
@article{wu2026adinteractome,
title = {{ADInteractome: discovering Alzheimer's disease-associated gene interactions from single-nucleus RNA-seq data through neural interaction states.}},
author = {Yifan Wu and Neha Maurya and Yuntao Yang and Wenbo Chen and Guanchu Wang and Zirui Liu and Xia Hu and W Jim Zheng and Zhaozhuo Xu},
journal = {Briefings in bioinformatics},
year = {2026},
month = nov,
doi = {10.1093/bib/bbag601},
url = {https://europepmc.org/article/MED/42969854},
note = {CC BY},
}
RIS
TY - JOUR
TI - ADInteractome: discovering Alzheimer's disease-associated gene interactions from single-nucleus RNA-seq data through neural interaction states.
AU - Yifan Wu
AU - Neha Maurya
AU - Yuntao Yang
AU - Wenbo Chen
AU - Guanchu Wang
AU - Zirui Liu
AU - Xia Hu
AU - W Jim Zheng
AU - Zhaozhuo Xu
PY - 2026
DA - 2026/11/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag601
UR - https://europepmc.org/article/MED/42969854
AB - Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by amyloid $\beta $ plaques and tau tangles, yet its molecular mechanisms remain elusive. Advances in single-nucleus RNA sequencing (snRNA-seq) have provided profound insights into the cellular heterogeneity of AD-associated brains. In this study, we introduce ADInteractome, an interpretable artificial intelligence framework designed to discover gene interactions associated with AD directly from the internal state of neural networks trained on the snRNA-seq data of brain cells. The method is based on the intuition that if a neural network can accurately classify cells from AD and non-AD donors based on their snRNA-seq data, then its internal interaction states, reflecting interaction patterns within the gene expression data, are informative for identifying biologically meaningful gene interactions. Applied to a large-scale snRNA-seq dataset from the Seattle Alzheimer's Disease Brain Cell Atlas, ADInteractome reveals cell-type-specific and AD-associated gene circuits supported by existing literature. The results were further supported by our virtual knock-out and knock-in experiments, a novel concept to probe our findings through a data-driven approach. Compared with traditional co-expression network methods, it achieves stronger enrichment for known AD-associated gene interactions and uncovers novel interaction patterns. These findings highlight the potential of interpretable AI approaches for elucidating complex molecular interactions in neurodegenerative disease.
SN - 1467-5463
SN - 1477-4054
ER -