GT-Mamba: A Topology-Aware Graph-State Space Model for Robust and Interpretable Epigenetic Age Prediction

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ID: 317506
2026
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Abstract
Abstract Motivation Current epigenetic clocks face a trade-off between predictive accuracy and biological interpretability, often relying on dataset-specific correction to generalize across cohorts. 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. Results GT-Mamba demonstrates strong out-of-the-box robustness across heterogeneous independent validation cohorts, achieving a weighted average MAE of 4.43 years. Notably, it effectively generalizes to EPIC 850k arrays despite partial feature missingness, and maintains consistent performance across homologous age distribution shifts (MAE 2.94 years 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. Availability 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). Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164933082 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Han Wang, Hui Wang, Yanting Tong, Yuanyuan Liu, Qu Jing, Guan Ning Lin, Li Zhang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag401
URL
https://doi.org/10.1093/bioinformatics/btag401
Keywords Keywords not found

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