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