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September 29, 2025

G4STAB: A multi-input deep learning model to predict G-quadruplex thermodynamic stability based on sequence and salt concentration.

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Authors

DLDonn LiewADAkesha Dinuli DharmatillekeESE.S.K. See

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Overview

G4STAB predicts thermodynamic stability in g-quadruplexes based on sequence features and salt concentration, indicating potential therapeutic targets.

Key Points

  • G4STAB achieves high accuracy with R2 = 0.8 for predicting G-quadruplex melting temperatures based on DNA sequences.
  • The model incorporates diverse factors like salt concentration and pH to better estimate thermodynamic stability in cellular contexts.
  • Analysis of 391,502 validated G-quadruplexes shows significant changes in stability profiles under cancer-like ionic environments.
  • The findings point to systematic genomic patterns in G-quadruplex stability responses across different chromosomes.

Cite This Study

Liew et al. (2025) studied this question.

synapsesocial.com/papers/68da58d1c1728099cfd10c99https://doi.org/10.1093/bioinformatics/btaf545
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Sequence Determinants of G-Quadruplex Thermostability: Aligning Evidence from High-Precision Biophysics and High-Throughput Genomics2025
  2. 2Stability Matters: Revealing Causal Roles of G-Quadruplexes (G4s) in Regulation of Chromatin and Transcription2025
  3. 3G4-Attention: Deep Learning Model with Attention for predicting DNA G-Quadruplexes2024
  4. 4iDualG4: A Dual-Channel Deep Learning Framework for Predicting In Vivo G-Quadruplexes2026
  5. 5TransG4: an interpretable deep-learning approach for sequence-based G-quadruplex prediction2026