生物知情的神经网络模型通过自我修剪对虚假相互作用具有强大耐受性
Olof Nordenstorm1, Hratch Baghdassarian2, Douglas A Lauffenburger2
1Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, 17177, Sweden.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
生物信息神经网络 (BINNs) 可以通过自修剪虚假相互作用来识别可靠的细胞机制. 这种强大的方法增强了疾病研究和治疗策略的计算模型.
科学领域:
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
- 医学中的人工智能
背景情况:
- 细胞网络的计算模型对于理解疾病机制和开发疗法至关重要.
- 生物信息神经网络 (BINNs) 将深度学习与先前的生物知识相结合,以构建这些模型.
- 一个关键的挑战是验证由于细胞复杂性和未知的相互作用而推断的机制的可靠性.
研究的目的:
- 开发和评估一个整体方法来评估由BINNs推断的机制的可靠性.
- 引入和量化BINNs在培训期间作为可靠性指标的虚假相互作用的"自我修剪".
- 提高现有的BINN框架的大规模分析的计算效率.
主要方法:
- 实现了LEMBAS (大型知识嵌入式人工信号网络) 的GPU加速版本,用于细胞内信号动态,实现了超过7倍的速度.
- 在先前知识网络 (PKN) 中引入了故意的虚假交互,并在培训期间测量了BINN的删除 (自修剪).
- 在三个不同的数据集中评估了自我修剪指标,将L2规范化应用于模型.
主要成果:
- 用GPU加速的LEMBAS实现保持了预测准确性,同时显著提高了计算速度.
- 与PKN中存在的相比,BINNs对随机引入的虚假相互作用表现出更大的自我修剪程度.
- 自修剪指标被证明是可扩展的,可通用的,独立于手动策划,适用于不同的网络设置.
结论:
- 自修剪作为BINN强度的定量指标,用于预先生物知识中的不确定性.
- 这一指标表明,BINNs可以通过区分真实相互作用与噪音,有效地模拟真实生物系统.
- 增强的LEMBAS框架和自修剪指标为推进计算系统生物学提供了强大,高效的工具.
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