Scholarlink

<p>Precision oncology faces critical challenges in interpreting complex cellular signals and predicting drug responses across heterogeneous cancer environments. Here, we present BioGDR, a multimodal interpretable deep learning framework that integrates structure-based predicted biological features, including differential gene expression and kinase inhibition profiles, eliminating the need for experimental measurements. By modeling tumor transcriptomic states through pathway-informed graph neural networks and employing a drug-guided attention strategy, BioGDR enables mechanistic insights into drug sensitivity across compound and cellular contexts. Comprehensive evaluations demonstrate that BioGDR outperforms existing methods in compound screening relevant to early-stage drug discovery and in predicting cell line sensitivity across heterogeneous cellular states characteristic of precision oncology, while analyses on clinical patient cohorts further confirm its practical utility and generalization capability. Experimental validation with a novel ALDH1B1 inhibitor confirms its ability to identify sensitive cell populations and reveal underlying mechanisms. This work establishes a robust, biologically informed framework that bridges preclinical drug development and clinical applications, advancing precision oncology through integrative, multimodal learning and interpretable mechanism analysis.</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

Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

查看全文
  • Ning Qu,
  • Xiaochu Tong,
  • Zhaokun Wang,
  • Panpan Shao,
  • Lehan Zhang,
  • Xiaoya Zhang,
  • Yuxin Xing,
  • Jin Liu,
  • Yitian Wang,
  • Sulin Zhang,
  • Mingyue Zheng,
  • Xutong Li

摘要

Precision oncology faces critical challenges in interpreting complex cellular signals and predicting drug responses across heterogeneous cancer environments. Here, we present BioGDR, a multimodal interpretable deep learning framework that integrates structure-based predicted biological features, including differential gene expression and kinase inhibition profiles, eliminating the need for experimental measurements. By modeling tumor transcriptomic states through pathway-informed graph neural networks and employing a drug-guided attention strategy, BioGDR enables mechanistic insights into drug sensitivity across compound and cellular contexts. Comprehensive evaluations demonstrate that BioGDR outperforms existing methods in compound screening relevant to early-stage drug discovery and in predicting cell line sensitivity across heterogeneous cellular states characteristic of precision oncology, while analyses on clinical patient cohorts further confirm its practical utility and generalization capability. Experimental validation with a novel ALDH1B1 inhibitor confirms its ability to identify sensitive cell populations and reveal underlying mechanisms. This work establishes a robust, biologically informed framework that bridges preclinical drug development and clinical applications, advancing precision oncology through integrative, multimodal learning and interpretable mechanism analysis.