ADInteractome: discovering Alzheimer's disease-associated gene interactions from single-nucleus RNA-seq data through neural interaction states

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.