DVFGCDR:用于预测癌症药物反应的双视图融合图神经网络
Weihong Huang1,2, Shengwei Qin1, Haohao Li2
1School of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000, China.
NAR cancer
|November 21, 2025
概括
这项研究引入了DVFGCDR,这是通过结合药物特性和基因表达数据来预测癌症药物反应 (CDR) 的新模型. DVFGCDR显著提高了预测准确度,帮助个性化癌症治疗策略.
科学领域:
- 计算生物学 计算生物学
- 药物基因组学 药物基因组学
- 药物发现 药物发现 药物发现
背景情况:
- 预测癌症药物反应 (CDR) 对个性化医学至关重要,但癌症的细胞异质性提出了挑战.
- 当前的模型往往难以有效地整合多样化的分子数据,以准确地预测CDR.
研究的目的:
- 开发一种新的分子图卷积模型,DVFGCDR,用于更好地预测癌症药物反应.
- 整合多模式药物特征 (2D化学,3D几何) 和基因表达数据,以提高预测准确度.
主要方法:
- 开发了DVFGCDR,一个分子图形卷积网络.
- 嵌入2D化学和3D几何药物特性用于药物嵌入.
- 来自癌症细胞系的单细胞和大量RNA测序数据的综合数据.
主要成果:
- 在CDR分类和回归任务中,DVFGCDR表现出卓越的性能.
- 获得了高的皮尔森得分0.959,超过了现有的最先进的模型.
- 该模型集成多种数据源的能力提高了预测能力.
结论:
- DVFGCDR提供了一种强大而准确的方法来预测癌症药物反应.
- 这种模式有可能显著推进个性化癌症治疗和药物开发.
- 多模式数据的整合是克服CDR预测挑战的关键.
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