利用结构化生物知识进行反事实推理--以病毒发病机制为例
Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis
摘要：
反事实推理是比较复杂系统干预结果的有用工具。它要求我们以结构因果模型的形式来表示系统，并配有因果图、外生变量的概率假设和功能分配。在实践中，验证这样的模型可能非常困难。该过程需要大量的领域专业知识，并且不容易扩展到大型系统、多个系统或新的系统修改。与此同时，许多应用领域，如分子生物学，丰富的结构化因果知识，是定性的性质。本文提出了一种查询因果生物知识图的通用方法，并将定性结果转换为定量结构因果模型，该模型可以从数据中学习来回答问题。我们证明了这种方法的可行性，准确性和通用性，使用两个案例研究系统生物学。第一种方法证明了基本假设的适当性和结果的准确性。第二个演示的多功能性的方法，查询知识库的分子决定因素的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）诱导的细胞因子风暴，并进行反事实推理，以估计的因果关系影响的医疗对策重症患者。
英文摘要：
Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. We demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to estimate the causal effect of medical countermeasures for severely ill patients.
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