MLG2Net:分子全球图形网络用于肺癌细胞系中药物反应预测
Thi-Oanh Tran1,2, Thanh-Huy Nguyen2,3, Tuan Tung Nguyen1
1Hematology and Blood Transfusion Center, Bach Mai Hospital, Hanoi, Viet Nam.
Journal of medical systems
|April 10, 2025
概括
新型深度学习模型MLG2Net使用图形神经网络和药物基因组学数据准确预测肺癌细胞系的药物反应,优于现有方法.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物反应预测 (DRP) 对精准医学至关重要,深度学习 (DL) 显示出前景.
- 在DRP中的挑战包括数据质量,高维度和多omics集成,特别是癌症细胞系.
研究的目的:
- 引入MLG2Net,一种DL模型,利用图形神经网络在肺癌细胞系中用于DRP.
- 利用药物基因组学数据,通过SMILES表示药物和细胞系作为基因组图.
主要方法:
- 开发了MLG2Net,一个基于图形神经网络的DL模型.
- 纳入本地和全球图形网络用于药物 SMILES 代表.
- 利用细胞系基因组学作为输入特征.
主要成果:
- 在肺腺癌 (LUAD) 的DRP中,MLG2Net的表现优于三个参考图形网络.
- 获得了0.8616的皮尔森相关系数和2.94e-6的RMSE,用于LUAD.
- 在肺状细胞癌 (LUSC) 上表现较低,因为数据集大小较小.
- 确定全球网络组件特别有效.
结论:
- MLG2Net显示了DRP在癌症细胞系中的显著潜力.
- 模型性能受到数据集大小和特征的影响.
- 未来的改进可能涉及更大的数据集和精细的模型架构.
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