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arXiv 2604.09403q-bio.MN
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布尔网络基因调控模型中高效Shapley值计算

Efficient Shapley values computation for Boolean network models of gene regulation

University of Pisa(比萨大学)

Giang Pham, Silvia Giulia Galfrè, Paolo Milazzo

AI总结:

本文提出基于Shapley值的框架,用于评估布尔网络中节点对目标节点的重要性,通过传播方法高效计算Knock-out和Knock-in Shapley值,适用于有向无环网络并提供循环网络的良好近似。

摘要:

识别生物网络中动态影响节点是系统生物学中的核心问题,特别是在基因调控网络中优先确定干预目标。本文提出了一种基于Shapley值的框架,用于评估布尔网络中节点相对于给定目标节点的重要性。该框架包含两个互补的度量:Knock-out和Knock-in Shapley值。此外,我们提出了一种基于传播的方法,能够高效计算这些值。通过利用网络的逻辑结构,该方法避免了穷尽模拟。该方法在无环网络中是精确的,在有环网络中提供良好的近似。在Cell Collective数据库的基准模型上评估表明,传播方法能够准确恢复节点重要性排名并实现显著的速度提升。

英文摘要:

Identifying dynamically influential nodes in biological networks is a central problem in systems biology, particularly for prioritizing intervention targets in gene regulatory networks. In this paper, we propose a Shapley-value-based framework for assessing the importance of nodes in a Boolean network with respect to a given target node. The framework comprises two complementary measures: the Knock-out and the Knock-in Shapley values. Moreover, we present a propagation-based method that enables their efficient computation. By exploiting the logical structure of the network, the method avoids exhaustive simulations. The approach is exact for acyclic networks and provides good approximations for cyclic networks. Evaluation on benchmark models from the Cell Collective database shows that the propagation method accurately recovers node importance rankings while achieving substantial speed-ups.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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