ABSTRACT
The Mondo Disease Ontology (Mondo) represents cross-species diseases, which integrates several source disease terminologies to represent cross-species diseases, and provides precise semantic mappings to the original sources. Mondo spans both rare and 'common' diseases, as well as monogenic, acquired, neoplasms, infectious diseases, and more. Mondo is a community resource and is continuously updated and iteratively curated. Recent efforts sought to improve the representation of viral infectious diseases in Mondo, to properly represent primary infections, diseases caused by reactivation of a latent virus, such as shingles and diseases caused by aftereffects of a primary infection such as long COVID-19. This included the addition of new classes and new relations (object properties), and the creation of new design patterns. © 2021 Copyright for this paper by its authors.
ABSTRACT
Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks;the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.