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
Yuexu Jiang1, Manish Sridhar Immadi2, Duolin Wang1
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.
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.
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.
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