Organizing knowledge to enable faster data interpretation in COVID-19 research

Abstract

Enormous volumes of COVID-19 research data have been published and this continues to increase daily. This creates challenges for researchers to interpret, prioritize and summarize their own findings in the context of published literature, clinical trials, and a multitude of databases. Overcoming the data interpretation bottleneck is vital to help researchers to be more efficient in their quest to identify COVID-19 risk factors, potential treatments, drug side-effects, and much more. As a proof of concept, we have organized and integrated a range of COVID-19 and human biomedical data and literature into a knowledge graph (KG). Here we present the datasets we have integrated so far and the content of the KG which consists of 674,969 biological concepts and over 1.6 million relationships between them. The COVID-19 KG is available via KnetMiner, an interactive online platform for gene discovery and knowledge mining, or via RDF and Neo4j graph formats which can be searched programmatically through SPARQL and Cypher endpoints. KnetMiner is a road mapped ELIXIR UK service. We hope this integrated resource will enable faster data interpretation and discovery of linkages between genes, drugs, diseases and many more types of information relating to COVID-19.