Integrating network medicine and foundation models reveals cell-type-specific regulatory alterations in Alzheimer's disease.

Alzheimer's disease is a complex neurodegenerative disorder characterized by progressive cognitive decline and neuroinflammation. Although its molecular hallmarks are well documented, cell-type-specific mechanisms driving gene dysregulation remain elusive. While single-cell RNA sequencing resolves cellular states, most studies focus on individual genes rather than coordinated programs. Moreover, a gap persists between interpretable network-based models and artificial intelligence foundation models, which capture complex interactions but lack mechanistic transparency. Whether these approaches converge or provide complementary views remains unclear. We present an integrated study combining SCANet, for reconstructing co-expression and gene regulatory networks, with scGPT foundation model. Applied to over 1.3 million cells across 18 cell types, this approach revealed that Alzheimer-associated transcriptional changes concentrate within coherent co-expression modules, for extracellular matrix organization, immune signaling, and neuronal communication. Genes prioritized by scGPT were largely embedded within SCANet modules, indicating convergence at the gene level; however, higher-order architecture agreement was limited and cell-type-specific. Thus, scGPT highlights influential genes, whereas SCANet resolves their modular organization, providing complementary information. By integrating both methods, we recovered known Alzheimer-related pathways and identified novel regulatory candidates, including the CEBPB-CENPQ axis in vulnerable SST-GABA interneurons. This demonstrates that combining network biology with foundation models enables gene prioritization and mechanistic interpretation.