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In 2020, the White House released the, "Call to Action to the Tech Communityon New Machine Readable COVID-19 Dataset," wherein artificial intelligenceexperts are asked to collect data and develop text mining techniques that canhelp the science community answer high-priority scientific questions related toCOVID-19. The Allen Institute for AI and collaborators announced theavailability of a rapidly growing open dataset of publications, the COVID-19Open Research Dataset (CORD-19). As the pace of research accelerates,biomedical scientists struggle to stay current. To expedite theirinvestigations, scientists leverage hypothesis generation systems, which canautomatically inspect published papers to discover novel implicit connections.We present an automated general purpose hypothesis generation systems AGATHA-Cand AGATHA-GP for COVID-19 research. The systems are based on graph-mining andthe transformer model. The systems are massively validated using retrospectiveinformation rediscovery and proactive analysis involving human-in-the-loopexpert analysis. Both systems achieve high-quality predictions across domains(in some domains up to 0.97% ROC AUC) in fast computational time and arereleased to the broad scientific community to accelerate biomedical research.In addition, by performing the domain expert curated study, we show that thesystems are able to discover on-going research findings such as therelationship between COVID-19 and oxytocin hormone.

Accelerating COVID-19 research with graph mining and transformer-based learning