一个可解释的深度学习框架,用于复杂疾病生存结果中的生物标志物发现
Shiyu Wan1, Xinlei Mi2, Fei Zou1,3
1Department of Biostatistics, University of North Carolina at Chapel Hill.
bioRxiv : the preprint server for biology
|November 19, 2025
概括
新型深度学习框架SurvDNN准确识别了复杂疾病生存的生物标志物. 它提高了准确医学应用的预测准确性和模型稳定性.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 计算生物学是一种计算生物学.
- 基因组学和生物信息学
背景情况:
- 确定复杂疾病生存的生物标志物对于理解疾病机制和推进精准医学至关重要.
- 时间到事件数据由于其复杂性而带来挑战,包括非线性交互和高维度.
- 传统的生存数据建模方法与这些复杂性作斗争.
研究的目的:
- 提出SurvDNN,一个针对生存结果建模的深度神经网络框架.
- 为了增强模型的稳定性和减轻生存数据分析中的过拟合.
- 通过扩展的基于变的特征重要性测试 (PermFIT) 实现可解释的生物标志物发现.
主要方法:
- 开发了SurvDNN,这是一个用于生存结果的深度神经网络框架.
- 整合了基于引导的规范化,以减少过度装配.
- 实现了一个以稳定性为导向的过算法,以提高模型的稳定性.
- 基于变的扩展特征重要性测试 (PermFIT) 用于可解释的生物标志物量化.
主要成果:
- 与现有的机器学习方法相比,SurvDNN在模拟和现实数据中表现出更高的性能.
- 在生物标志物识别和预测建模方面实现了更高的准确性.
- PermFIT提供了对个体生物标志物贡献的可靠量化.
结论:
- SurvDNN与PermFIT相结合,为生物标志物驱动的生存建模提供了一个强大,可解释和强大的工具.
- 该框架在癌症和心血管疾病等复杂疾病的精准医学方面具有重大潜力.
- 为SurvDNN提供了一个开源的R包,供公众使用.
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