Scholarlink

Background <p>Circular RNAs (circRNAs) are an emerging class of non-coding RNAs with covalently closed loop structures and have been increasingly recognized for their regulatory roles in disease progression and drug response. Accurately identifying circRNA–drug sensitivity associations is therefore essential for understanding therapeutic mechanisms and advancing precision medicine. However, most existing computational methods fail to effectively integrate semantic and structural information and overlook cross-modal feature co-optimization, thereby limiting their predictive performance.</p> Results <p>To address these limitations, we develop an end-to-end graph representation learning framework for circRNA–drug sensitivity prediction by jointly modeling homogeneous similarity structures and heterogeneous interaction relationships. The framework integrates fused similarity graphs, semantic feature encoding with pre-norm residual attention, and structural representation learning via graph convolutional networks with Top-K sparse adjacency. In addition, a large-scale heterogeneous graph and a cross-modal collaborative feature mining module are employed to jointly optimize multi-source representations. Experimental results from 5-fold and 10-fold cross-validation, independent test evaluations, ablation study, and case study demonstrate that the proposed framework consistently achieves superior performance compared with state-of-the-art methods.</p> Conclusions <p>The proposed framework provides a robust and effective computational strategy for circRNA–drug sensitivity prediction and offers a valuable tool for uncovering potential therapeutic associations, thereby facilitating future research in drug response analysis and precision medicine.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA–drug sensitivity association prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

查看全文
  • Chao Cao,
  • Mengli Li,
  • Maozu Guo,
  • Chunyu Wang,
  • Quan Zou,
  • Mengting Niu

摘要

Background

Circular RNAs (circRNAs) are an emerging class of non-coding RNAs with covalently closed loop structures and have been increasingly recognized for their regulatory roles in disease progression and drug response. Accurately identifying circRNA–drug sensitivity associations is therefore essential for understanding therapeutic mechanisms and advancing precision medicine. However, most existing computational methods fail to effectively integrate semantic and structural information and overlook cross-modal feature co-optimization, thereby limiting their predictive performance.

Results

To address these limitations, we develop an end-to-end graph representation learning framework for circRNA–drug sensitivity prediction by jointly modeling homogeneous similarity structures and heterogeneous interaction relationships. The framework integrates fused similarity graphs, semantic feature encoding with pre-norm residual attention, and structural representation learning via graph convolutional networks with Top-K sparse adjacency. In addition, a large-scale heterogeneous graph and a cross-modal collaborative feature mining module are employed to jointly optimize multi-source representations. Experimental results from 5-fold and 10-fold cross-validation, independent test evaluations, ablation study, and case study demonstrate that the proposed framework consistently achieves superior performance compared with state-of-the-art methods.

Conclusions

The proposed framework provides a robust and effective computational strategy for circRNA–drug sensitivity prediction and offers a valuable tool for uncovering potential therapeutic associations, thereby facilitating future research in drug response analysis and precision medicine.