Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach
| Autor: | M. Sohel Rahman, Hamada R. H. Al-Absi, Junaed Younus Khan, Iram Tazim Hoque, Tawkat Islam Khondaker, Tanvir Alam, Mohammad Saifur Rahman, Reto Guler |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Drug hydroxychloroquine Coronavirus disease 2019 (COVID-19) Computer science media_common.quotation_subject Computer applications to medicine. Medical informatics 030106 microbiology R858-859.7 coronavirus remdesivir dexamethasone Health Informatics Scientific literature statins Scientific evidence ivermectin 03 medical and health sciences Health Information Management media_common SARS Original Paper SARS-CoV-2 business.industry Cosine similarity Sentiment analysis statin COVID-19 Data science 030104 developmental biology Open source Knowledge base 2019-nCoV business |
| Zdroj: | JMIR Medical Informatics JMIR Medical Informatics, Vol 8, Iss 11, p e21648 (2020) |
| ISSN: | 2291-9694 |
| DOI: | 10.2196/21648 |
| Popis: | Background Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion. Objective The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach. Methods We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes. Results Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base. Conclusions Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19. |
| Databáze: | OpenAIRE |
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