Perturbation experiments allow biologists to discover causal relationships between variables of interest, but the sparsity and high dimensionality of these data pose significant challenges for causal structure learning algorithms. Biological knowledge graphs can bootstrap the inference of causal structures in these situations, but since they compile vastly diverse information, they can bias predictions towards well-studied systems. Alternatively, amortized causal structure learning algorithms encode inductive biases through data simulation and train supervised models to recapitulate these synthetic graphs. However, realistically simulating biology is arguably even harder than understanding a specific system. In this work, we take inspiration from both strategies and propose an amortized algorithm for refining domain knowledge, based on data observations. On real and synthetic datasets, we show that our approach outperforms baselines in recovering ground truth causal graphs and identifying errors in the prior knowledge with limited interventional data.

本研究解决了生物网络推断中由于数据稀疏性和高维性导致的因果关系学习难题。提出了一种基于数据观察的算法，通过精炼领域知识，结合了现有的生物知识图谱和模拟数据的方法。实验结果表明，该算法在揭示真实因果图和识别先前知识错误方面优于传统基线算法，即使在有限的干预数据条件下。 

学习精炼生物网络推断的领域知识