aceG4-Attention: an interpretable and lightweight deep learning framework for DNA G-quadruplex prediction
G-quadruplexes (G4s) are four-stranded non-canonical nucleic acid secondary structures, formed by the stacking arrangement of guanine tetramers. They are involved in a wide range of biological processes because of their exceptionally unique and distinct structural characteristics. After the completion of the human genome sequencing project, a lot of bio-informatics algorithms were introduced to predict the active G4 regions in vitro based on the canonical G4 sequence elements, G-richness and G-skewness, as well…
A lightweight CNN, Bi-LSTM and attention model predicts G-quadruplex-forming sequences more accurately than earlier shallow CNN models, including on imbalanced data and cell-type-specific tasks.
Performance is reduced for evolutionarily distant prokaryotes, so the human-trained model does not generalize equally across all species.
Evidence
- Peer-reviewedPhysical Chemistry Chemical Physics2026-10-14
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Truvace Impact Record TRV-2026-1927, v1: “G4-Attention: an interpretable and lightweight deep learning framework for DNA G-quadruplex prediction.” Truvace, 2026-10-19. /record/TRV-2026-1927 (accessed at citation time). sha256 487b70af121293fa…
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