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IRnet:使用途径知识为基础的图形神经网络进行免疫疗法反应预测.

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  • 1Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO, USA; Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO, USA.

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概括
此摘要是机器生成的。

一个新的深度学习模型,IRnet,准确地预测哪些癌症患者会对免疫检查点抑制剂 (ICI) 产生反应. 这种工具有助于节约资源和确定有效的免疫疗法治疗方法.

关键词:
生物途径 生物途径检查点抑制剂检查点抑制剂图表神经网络的神经网络免疫疗法的反应反应.机器学习 机器学习模型的解释性 模型的解释性

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 免疫检查点抑制剂 (ICI) 为癌症患者提供了显著的生存益处.
  • 然而,只有很少一部分患者对ICI有反应,因此需要预测性生物标志物.

研究的目的:

  • 开发一种新的深度学习 (DL) 方法,用于预测患者对ICI的反应.
  • 通过识别响应者进行治疗前,改善资源配置并最大限度地减少不良影响.

主要方法:

  • 开发了一个DL框架,集成图形神经网络和生物通路知识.
  • 该模型是根据黑色素瘤,胃癌和膀癌患者的临床试验数据进行训练和验证的,这些患者接受了ICI治疗.

主要成果:

  • 与现有的最先进的和基于瘤微环境的预测指标相比,IRnet模型显示出更高的性能.
  • 该模型通过量化基因,通路及其相互作用的重要性来提供可解释性.
  • 部署了一个公开可访问的Web服务器 (https://irnet.missouri.edu).

结论:

  • IRnet是一种有效的工具,用于预测患者对免疫检查点抑制剂治疗的反应.
  • 该模型的可解释性为ICI治疗疗效的机制提供了洞察力.