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Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding.

Heyoung Yang1, Eunsoo Sohn1

  • 1Future Technology Analysis Center, Korea Institute of Science and Technology Information, 66, Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea.

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|April 3, 2021
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Artificial intelligence and machine learning analyze biomedical literature to understand coronavirus disease 2019 (COVID-19). This approach identifies potential anti-infective drugs and proteins for COVID-19 treatment development.

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COVID-19PubMed literaturedrug repurposingmachine learningmedical subject headingssubstance nameword embedding

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence
  • Computational Biology

Background:

  • Understanding coronavirus disease 2019 (COVID-19) clinical characteristics is crucial for managing the pandemic.
  • Existing knowledge gaps hinder the development of effective vaccines and treatments.
  • Biomedical literature contains vast information that can be leveraged for insights.

Purpose of the Study:

  • To apply artificial intelligence (AI) methods to infer characteristics of COVID-19 from biomedical literature.
  • To develop a rapid method for identifying potential therapeutic targets and drugs related to COVID-19.
  • To provide guidelines for researchers and pharmaceutical companies in their pursuit of COVID-19 treatments.

Main Methods:

  • Constructed a biomedical knowledge base using FastText word embedding on a decade of PubMed literature.
  • Updated the knowledge base with recent COVID-19 research articles.
  • Inferred relationships between COVID-19, anti-infective drugs, and proteins based on proximity in the knowledge base.

Main Results:

  • Identified a list of anti-infective drugs potentially related to COVID-19.
  • Inferred potential human and coronavirus proteins associated with COVID-19.
  • Demonstrated a method for quickly inferring relevant information from existing knowledge bases.

Conclusions:

  • AI-powered analysis of biomedical literature can accelerate understanding of emerging diseases like COVID-19.
  • The developed method offers a valuable approach for identifying potential therapeutic interventions.
  • Machine learning research in PubMed literature provides a strategic direction for ongoing COVID-19 treatment development.