论文标题
基于本体的系统分类和分析冠状病毒，宿主和宿主 - 核纳病毒相互作用，以深入了解Covid-19
Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19
论文作者
论文摘要
鉴于全球现有的Covid-19大流行，系统地研究宿主与冠状病毒之间的相互作用至关重要，包括SARS-COV，MERS-COV和SARS-COV-2（COVID-19的原因）。我们首先创建了四种宿主 - 病原体相互作用（HPI） - outcome假设，并产生了HPI结果模型，作为理解宿主 - 核方纳病毒相互作用（HCI）及其与疾病结果的关系的基础。我们假设本体可以用作一个综合平台，以对HCI和疾病结果进行分类和分析。因此，我们使用本体学注释并分类了不同的冠状病毒，宿主和表型，并确定了它们的关系。假设各种COVID-19表型是由后端HCI机制引起的。为了进一步识别因果HCI结果关系，我们收集了35个实验验证的HCI蛋白 - 蛋白质相互作用（PPI），并应用了文献挖掘，以鉴定对冠状病毒感染的其他宿主PPI。结果是在逻辑本体论代表中提出的，以进行综合的HCI结果理解。使用已知的PPI作为诱饵，我们还开发并应用了域提取的预测方法来预测新的PPI并确定其在多个器官上的病理靶标。总体而言，我们提出的基于本体的综合框架与计算预测相结合，可用于支持对人与冠状病毒（包括SARS-COV-2）之间复杂相互作用的基本理解，以及它们与各种疾病结果的关联。
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.
