基于本体的冠状病毒、宿主和宿主-冠状病毒相互作用的系统分类和分析，以深入理解COVID-19
Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19
摘要：
鉴于目前COVID-19在全球范围内大流行，系统地研究宿主与冠状病毒（包括SARS-Cov、MERS-Cov和SARS-CoV-2（COVID-19的病因））之间的相互作用至关重要。我们首先创建了四个宿主-病原体相互作用（HPI）-结果假设，并生成了一个HPI-结果模型，作为理解宿主-冠状病毒相互作用（HCI）及其与疾病结果关系的基础。我们假设本体论可以作为一个综合平台来分类和分析HCI和疾病结果。因此，我们使用本体对不同的冠状病毒、宿主和表型进行了注释和分类，并确定了它们之间的关系。假设各种COVID-19表型是由后端HCI机制引起的。为了进一步确定HCI-结果的因果关系，我们收集了35个实验验证的HCI蛋白质相互作用（PPI），并应用文献挖掘来确定其他宿主PPI对冠状病毒感染的反应。结果制定在一个逻辑本体表示为一体的HCI结果的理解。使用已知的PPI作为诱饵，我们还开发并应用了一种域推断预测方法来预测新的PPI并确定其在多个器官上的病理靶点。总的来说，我们提出的基于本体的综合框架与计算预测相结合，可用于支持对人类患者与冠状病毒（包括SARS-CoV-2）之间错综复杂的相互作用及其与各种疾病结果之间的关联的基本理解。
英文摘要：
Given the existing COVID-19 pandemic worldwide, it is critical to systematically study the interactions between hosts and coronaviruses including SARS-Cov, MERS-Cov, and SARS-CoV-2 (cause of COVID-19). We first created four host-pathogen interaction (HPI)-Outcome postulates, and generated a HPI-Outcome model as the basis for understanding host-coronavirus interactions (HCI) and their relations with the disease outcomes. We hypothesized that ontology can be used as an integrative platform to classify and analyze HCI and disease outcomes. Accordingly, we annotated and categorized different coronaviruses, hosts, and phenotypes using ontologies and identified their relations. Various COVID-19 phenotypes are hypothesized to be caused by the backend HCI mechanisms. To further identify the causal HCI-outcome relations, we collected 35 experimentally-verified HCI protein-protein interactions (PPIs), and applied literature mining to identify additional host PPIs in response to coronavirus infections. The results were formulated in a logical ontology representation for integrative HCI-outcome understanding. Using known PPIs as baits, we also developed and applied a domain-inferred prediction method to predict new PPIs and identified their pathological targets on multiple organs. Overall, our proposed ontology-based integrative framework combined with computational predictions can be used to support fundamental understanding of the intricate interactions between human patients and coronaviruses (including SARS-CoV-2) and their association with various disease outcomes.
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