This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2554) Data Science (357) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1542) Artificial Intelligence (2129) Machine Learning (2076)

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

Authors of this article:

Chao Jiang1 Author Orcid Image ;   Victoria Ngo2, 3, 4 Author Orcid Image ;   Richard Chapman1 Author Orcid Image ;   Yue Yu5 Author Orcid Image ;   Hongfang Liu5 Author Orcid Image ;   Guoqian Jiang5 Author Orcid Image ;   Nansu Zong5 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

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  1. Chen H, Lu D, Xiao Z, Li S, Zhang W, Luan X, Zhang W, Zheng G. Comprehensive applications of the artificial intelligence technology in new drug research and development. Health Information Science and Systems 2024;12(1) View
  2. Zhao Y, Bollegala D, Hirose S, Jin Y, Kozu T. Community knowledge graph abstraction for enhanced link prediction: A study on PubMed knowledge graph. Journal of Biomedical Informatics 2024;158:104725 View
  3. Meng Y, Wang Y, Hu X, Lu C, Tang X, Cui F, Zeng P, Yao Y, Yang J, Xu J. Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics 2025;167:104843 View

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  4. Bhat V, Harsha T, Vinay S. Graph Neural Networks: Essentials and Use Cases. View