路径HDNN:一种路径层次信息深度神经网络框架,用于预测免疫疗法反应和机制解释
Xiangmei Li1, Bingyue Pan1, Yalan He1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Genome medicine
|December 2, 2025
概括
一个新的深度学习模型,PathHDNN,通过分析患者突变和途径数据,准确地预测癌症免疫治疗反应. 它确定了关键的生物通路,如p53和JAK2,这对治疗疗效至关重要.
科学领域:
- 计算生物学是一种计算生物学.
- 癌症研究 癌症研究
- 免疫学 免疫学 免疫学
背景情况:
- 免疫疗法为癌症患者提供了显著的生存益处,但预测反应仍然具有挑战性.
- 了解免疫疗法疗效的机制对于改善患者的治疗结果至关重要.
研究的目的:
- 开发一种新的深度学习框架,PathHDNN,用于预测免疫疗法反应.
- 确定与免疫疗法疗效相关的关键生物途径.
主要方法:
- 开发了一个路径等级信息深度神经网络 (PathHDNN) 框架.
- 编码生物路径等级,体突变和拷贝数变异到网络中.
- 优化网络边缘权重,同时保持生物解释性.
主要成果:
- 在预测跨队列免疫疗法反应方面,PathHDNN超过了现有的机器学习方法和生物标志物.
- 在黑色素瘤中确定了关键途径,包括p53介导的瘤抑制和JAK2介导的免疫调节.
- 证明了PathHDNN能够提供对治疗反应机制的可解释见解的能力.
结论:
- PathHDNN提供了一个有效的框架来预测患者对癌症免疫疗法的反应.
- 该模型为治疗反应的生物机制提供了宝贵的见解,进步了精准医学.
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