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We propose GT-Mamba, a novel architecture that integrates a Structure-Aware Graph Transformer with the Mamba state space model. This design captures CpG topological correlations and genome-wide long-range dependencies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>GT-Mamba demonstrates strong out-of-the-box robustness across heterogeneous independent validation cohorts, achieving a weighted average MAE of 4.43\u2009years. Notably, it effectively generalizes to EPIC 850k arrays despite partial feature missingness, and maintains consistent performance across homologous age distribution shifts (MAE 2.94\u2009years in a young cohort). Ablation studies confirm that graph topology contributes to improved robustness against noise. Mechanistic analysis suggests that the model captures methylation patterns associated with both developmental and functional processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability<\/jats:title>\n                    <jats:p>Source code and pre-trained models are freely available at https:\/\/github.com\/NENUBioCompute\/GT-Mamba and archived on Zenodo (DOI: 10.5281\/zenodo.19703155).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag401","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:25:12Z","timestamp":1781612712000},"source":"Crossref","is-referenced-by-count":0,"title":["GT-Mamba: a Topology-Aware Graph-State space model for robust and interpretable epigenetic age 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