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

Abstract

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 associations 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 system 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.

• Authors: Li Wang, Lei Jiang, Dongyan Pan, Qinghua Wang, Zeyu Yin, Zijian Kang, Haoran Tian, Xuqiang Geng, Jinsong Shao, Wenjie Pan, Jian Yin, Li Fang, Yue Wang, Weide Zhang, Zhixiu Li, Jun Zheng, Wenxin Hu, Yunbao Pan, Dong Yu, Shicheng Guo, Wei Lu, Qiang Li, Yunyun Zhou, Huji Xu
• Journal: Biomedical journal (Biomed J) Vol. 45 Issue 3 Pg. 472-481 (06 2022) ISSN: 2320-2890 [Electronic] United States
• PMID: 35367669 (Publication Type: Journal Article)
• Copyright: Published by Elsevier B.V.

Chemical References

• Peptidyl-Dipeptidase A

Topics

• COVID-19
• Humans
• Knowledge Discovery
• Natural Language Processing
• Peptidyl-Dipeptidase A (metabolism)
• SARS-CoV-2