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

Background: The impact of COVID-19 on public health has mandated an 'all hands on deck' scientific response. The current clinical study and basic research on COVID-19 are mainly based on existing publications or our knowledge of coronavirus. However, efficiently retrieval of accurate, relevant knowledge on COVID-19 can pose significant challenges for researchers.Methods: To improve quality in accessing important literature findings, we developed a novel natural language processing (NLP) method to automatically recognize the associa-tions among potential targeted host organ systems, associated clinical manifestations, and pathways. We further validated these associations through clinician experts' evaluations and prioritize candidate drug targets through bioinformatics network analysis.Results: We found that the angiotensin-converting enzyme 2 (ACE2), a receptor that SARS-CoV-2 required for cell entry, is associated with cardiovascular and endocrine organ sys-tem and diseases. Furthermore, we found SARS-CoV-2 is associated with some important pathways such as IL-6, TNF-alpha, and IL-1 beta-induced dyslipidemia, which are related to inflammation, lipogenesis, and oxidative stress mechanisms, suggesting potential drug candidates.Conclusion: We prioritized the list of therapeutic targets involved in antiviral and immune modulating drugs for experimental validation, rendering it valuable during public health crises marked by stresses on clinical and research capacity. Our automatic intelligence pipeline also contributes to other novel and emerging disease management and treatments in the future.

Automated summary

Researchers have developed a novel natural language processing method to automatically identify the associations between the novel coronavirus and cardiovascular and endocrine organ systems and diseases. They have also found important pathways related to inflammation, lipogenesis, and oxidative stress mechanisms, suggesting potential drug candidates.