Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38584, first published .
Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation

Chao Jiang   1 , BS ;   Victoria Ngo   2, 3, 4 , PhD ;   Richard Chapman   1 , PhD ;   Yue Yu   5 , PhD ;   Hongfang Liu   5 , PhD ;   Guoqian Jiang   5 , MD, PhD ;   Nansu Zong   5 , PhD

1 Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States

2 Center for Innovation to Implementation, VA Palo Alto Health Care System, Sacramento, CA, United States

3 Stanford Health Policy, Stanford School of Medicine, Stanford University, Stanford, CA, United States

4 Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, United States

5 Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, United States

Corresponding Author:

  • Nansu Zong, PhD
  • Department of Artificial Intelligence and Informatics Research
  • Mayo Clinic
  • 200 First St SW
  • Rochester, MN, 55905
  • United States
  • Phone: 1 507-284-2511
  • Email: Zong.Nansu@mayo.edu