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ICIsc:一个深度学习框架,通过集成scRNA-Seq和蛋白质语言模型来预测免疫检查点抑制剂反应.

Zhenyu Jin1, Di Zhang1, Luonan Chen1,2

  • 1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China.

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概括

一个新的深度学习框架,ICIsc,通过将单细胞RNA测序数据与蛋白质语言模型集成,准确地预测患者对癌症免疫疗法的反应. 这种方法提高了治疗预测,并确定了为更好的患者结果提供关键基因.

关键词:
注意力网络注意力网络检查点抑制剂检查点抑制剂深度学习是一种深度学习.免疫疗法反应的免疫疗法反应单一的简单网络 单一的简单网络一个单细胞RNA测序.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 免疫学 免疫学 免疫学
  • 在瘤学瘤学.

背景情况:

  • 免疫检查点抑制剂 (ICI),如PD-1/PD-L1和CTLA-4,改善了癌症存活率,但许多患者没有反应.
  • 准确预测ICI反应对于有效的癌症治疗至关重要.

研究的目的:

  • 开发一个深度学习框架 (ICIsc) 来预测患者对ICI的反应.
  • 整合单细胞RNA测序 (scRNA-seq) 数据与蛋白质大语言模型进行增强的预测.

主要方法:

  • ICIsc从转录形状和免疫基因组分数构建患者表征.
  • 药物表征是从ICI氨基酸序列中得出的,使用进化规模建模2 (ESM2).
  • 双线性注意模块将患者和药物嵌入组合为批量数据;对于scRNA-seq,单样网络 (SSN) 和GATv2模型免疫微环境异质性.

主要成果:

  • 在预测ICI响应方面,ICIsc显著优于基线模型.
  • 该框架在基准评估和独立验证方面表现出强大的概括性能.
  • SHAP分析确定了关键基因,如GAPDH,与免疫疗法反应和预后有关.

结论:

  • ICIsc提供了一个准确和可解释的计算框架,用于预测免疫治疗结果.
  • 该模型有助于理解患者对癌症免疫疗法的反应背后的机制.
  • 这种方法有可能优化接受免疫治疗的癌症患者的临床决策.