Leveraging Structured Biological Knowledge for Counterfactual Inference [electronic resource] : A Case Study of Viral Pathogenesis

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 576.8 Evolution

Thông tin xuất bản: Richland, Wash. : Oak Ridge, Tenn. : Pacific Northwest National Laboratory (U.S.) ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2021

Mô tả vật lý: Size: p. 25-37 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 261727

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 knowledge graph with a causal question and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. Here, 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 predict the causal effect of medical countermeasures for severely ill COVID-19 patients.
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