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Given the existing COVID-19 pandemic worldwide, it is critical tosystematically study the interactions between hosts and coronaviruses includingSARS-Cov, MERS-Cov, and SARS-CoV-2 (cause of COVID-19). We first created fourhost-pathogen interaction (HPI)-Outcome postulates, and generated a HPI-Outcomemodel as the basis for understanding host-coronavirus interactions (HCI) andtheir relations with the disease outcomes. We hypothesized that ontology can beused as an integrative platform to classify and analyze HCI and diseaseoutcomes. Accordingly, we annotated and categorized different coronaviruses,hosts, and phenotypes using ontologies and identified their relations. VariousCOVID-19 phenotypes are hypothesized to be caused by the backend HCImechanisms. To further identify the causal HCI-outcome relations, we collected35 experimentally-verified HCI protein-protein interactions (PPIs), and appliedliterature mining to identify additional host PPIs in response to coronavirusinfections. The results were formulated in a logical ontology representationfor integrative HCI-outcome understanding. Using known PPIs as baits, we alsodeveloped and applied a domain-inferred prediction method to predict new PPIsand identified their pathological targets on multiple organs. Overall, ourproposed ontology-based integrative framework combined with computationalpredictions can be used to support fundamental understanding of the intricateinteractions between human patients and coronaviruses (including SARS-CoV-2)and their association with various disease outcomes.

Ontology-based systematic classification and analysis of coronaviruses,  hosts, and host-coronavirus interactions towards deep understanding of  COVID-19