Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding
| Autor: | Eunsoo Sohn, Heyoung Yang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Biomedical knowledge Word embedding Coronavirus disease 2019 (COVID-19) Computer science Health Toxicology and Mutagenesis Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lcsh:Medicine Article 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Humans 030212 general & internal medicine medical subject headings 030304 developmental biology 0303 health sciences drug repurposing business.industry SARS-CoV-2 lcsh:R substance name Public Health Environmental and Occupational Health COVID-19 PubMed literature Base (topology) word embedding Data science machine learning Knowledge base business Coronavirus Infections |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 18 Issue 6 International Journal of Environmental Research and Public Health, Vol 18, Iss 3005, p 3005 (2021) |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph18063005 |
| Popis: | A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19. A biomedical knowledge base was established via FastText, a word embedding technique, using PubMed literature from the past decade. Subsequently, a new knowledge base was created using recently published COVID-19 articles. Using this newly constructed knowledge base from the word embedding model, a list of anti-infective drugs and proteins of either human or coronavirus origin were inferred to be related, because they are located close to COVID-19 on the knowledge base. This study attempted to form a method to quickly infer related information about COVID-19 using the existing knowledge base, before sufficient knowledge about COVID-19 is accumulated. With COVID-19 not completely overcome, machine learning-based research in the PubMed literature will provide a broad guideline for researchers and pharmaceutical companies working on treatments for COVID-19. |
| Databáze: | OpenAIRE |
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