Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning
Keywords:Natural Language Processing, Graph Mining, Deep Learning, Hypothesis Generation, Recommendation System, COVID-19, CORD-19
AbstractIn 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 automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on the graph mining and transformer models. 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 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 ongoing research findings such as the relationship between COVID-19 and oxytocin hormone. All code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP.
How to Cite
Tyagin, I., Kulshrestha, A., Sybrandt, J., Matta, K., Shtutman, M., & Safro, I. (2022). Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12673-12679. https://doi.org/10.1609/aaai.v36i11.21543
IAAI Technical Track on AI Integration