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Related Experiment Video

Updated: Sep 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Novel approach by natural language processing for COVID-19 knowledge discovery.

Li Wang1, Lei Jiang2, Dongyan Pan3

  • 1Medical School, Nantong University, Nantong, China; Research Center for Intelligence Information Technology, Nantong University, Nantong, China.

Biomedical Journal
|April 3, 2022
PubMed
Summary
This summary is machine-generated.

A novel natural language processing method identifies COVID-19 related organ systems, pathways, and potential drug targets. This approach aids researchers in efficiently accessing critical information during public health crises.

Keywords:
ACE2COVID-19Natural language processingSARS-COV-2TMPRSS2

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Public Health

Background:

  • COVID-19 research faces challenges in efficiently retrieving accurate, relevant knowledge from existing literature.
  • Current research relies heavily on prior knowledge of coronaviruses and published studies.
  • Effective information access is crucial for a rapid scientific response to the pandemic.

Purpose of the Study:

  • To develop a novel natural language processing (NLP) method for identifying associations between COVID-19, host organ systems, clinical manifestations, and biological pathways.
  • To validate identified associations using expert clinician evaluations.
  • To prioritize potential drug targets through bioinformatics network analysis.

Main Methods:

  • Developed a novel NLP pipeline to automatically recognize associations among targeted host organ systems, clinical manifestations, and pathways.
  • Validated identified associations through expert clinician evaluations.
  • Utilized bioinformatics network analysis to prioritize candidate drug targets.

Main Results:

  • Identified the angiotensin-converting enzyme 2 (ACE2) receptor's association with cardiovascular and endocrine systems/diseases.
  • Discovered SARS-CoV-2 associations with key pathways (IL-6, TNF-alpha, IL-1 beta) linked to inflammation and dyslipidemia.
  • Highlighted potential drug candidates based on identified pathways and mechanisms like oxidative stress.

Conclusions:

  • Prioritized therapeutic targets for antiviral and immune-modulating drugs, valuable for public health emergencies.
  • The NLP pipeline offers a valuable tool for managing and treating emerging diseases.
  • This approach enhances research capacity during health crises by streamlining knowledge discovery.