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March 27, 2026Physical Chemistry Chemical Physics

TransG4: an interpretable deep-learning approach for sequence-based G-quadruplex prediction

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Authors

YYYongna YuanYTYaojie TianZLZhenyu Liu

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Overview

Deep learning predicts G-quadruplex formation using sequence data, suggesting new insights into DNA and RNA structures.

Key Points

  • This research aims to develop a deep learning model to predict G-quadruplex formation from DNA and RNA sequences.
  • Utilized CNN, transformer, and BiGRU models for prediction
  • Analyzed G4-seq and rG4-seq data
  • Estimated DNA mismatch and RNA RSR scores
  • Identified sequence features using attention-derived motifs
  • Successfully predicted G-quadruplex formation propensity
  • Revealed sequence features associated with G4 structures through attention mechanisms
  • Estimated mismatch and RSR scores average and variability in significant sequences

Cite This Study

Yuan et al. (2026) studied this question.

synapsesocial.com/papers/69c620be15a0a509bde195cfhttps://doi.org/10.1039/d6cp00173d
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Also Consider

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

  1. 1G4-Attention: Deep Learning Model with Attention for predicting DNA G-Quadruplexes2024
  2. 2iDualG4: A Dual-Channel Deep Learning Framework for Predicting In Vivo G-Quadruplexes2026
  3. 3A Deep Learning Framework for Comprehensive Prediction of Human RNA G-Quadruplex-Binding Proteins2026
  4. 4G4STAB: A multi-input deep learning model to predict G-quadruplex thermodynamic stability based on sequence and salt concentration.2025
  5. 5G4mer: An RNA language model for transcriptome-wide identification of G-quadruplexes and disease variants from population-scale genetic data2024 · 1 citations