Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach
<h3>Background</h3><p dir="ltr">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 t...
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2020
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| _version_ | 1864513512845344768 |
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| author | Junaed Younus Khan (16870110) |
| author2 | Md Tawkat Islam Khondaker (18718810) Iram Tazim Hoque (18718813) Hamada R H Al-Absi (18718816) Mohammad Saifur Rahman (8922641) Reto Guler (368266) Tanvir Alam (638619) M Sohel Rahman (17473248) |
| author2_role | author author author author author author author |
| author_facet | Junaed Younus Khan (16870110) Md Tawkat Islam Khondaker (18718810) Iram Tazim Hoque (18718813) Hamada R H Al-Absi (18718816) Mohammad Saifur Rahman (8922641) Reto Guler (368266) Tanvir Alam (638619) M Sohel Rahman (17473248) |
| author_role | author |
| dc.creator.none.fl_str_mv | Junaed Younus Khan (16870110) Md Tawkat Islam Khondaker (18718810) Iram Tazim Hoque (18718813) Hamada R H Al-Absi (18718816) Mohammad Saifur Rahman (8922641) Reto Guler (368266) Tanvir Alam (638619) M Sohel Rahman (17473248) |
| dc.date.none.fl_str_mv | 2020-11-10T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.2196/21648 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Toward_Preparing_a_Knowledge_Base_to_Explore_Potential_Drugs_and_Biomedical_Entities_Related_to_COVID-19_Automated_Computational_Approach/25958059 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Cardiovascular medicine and haematology Oncology and carcinogenesis Information and computing sciences Artificial intelligence Software engineering COVID-19 2019-nCoV coronavirus SARS-CoV-2 SARS remdesivir statin statins dexamethasone ivermectin hydroxychloroquine |
| dc.title.none.fl_str_mv | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">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.</p><h3>Objective</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Medical Informatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/21648" target="_blank">https://dx.doi.org/10.2196/21648</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a10c61dd16956e66a436e48cd4345509 |
| identifier_str_mv | 10.2196/21648 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25958059 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational ApproachJunaed Younus Khan (16870110)Md Tawkat Islam Khondaker (18718810)Iram Tazim Hoque (18718813)Hamada R H Al-Absi (18718816)Mohammad Saifur Rahman (8922641)Reto Guler (368266)Tanvir Alam (638619)M Sohel Rahman (17473248)Biological sciencesBioinformatics and computational biologyBiomedical and clinical sciencesCardiovascular medicine and haematologyOncology and carcinogenesisInformation and computing sciencesArtificial intelligenceSoftware engineeringCOVID-192019-nCoVcoronavirusSARS-CoV-2SARSremdesivirstatinstatinsdexamethasoneivermectinhydroxychloroquine<h3>Background</h3><p dir="ltr">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.</p><h3>Objective</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Medical Informatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/21648" target="_blank">https://dx.doi.org/10.2196/21648</a></p>2020-11-10T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.2196/21648https://figshare.com/articles/journal_contribution/Toward_Preparing_a_Knowledge_Base_to_Explore_Potential_Drugs_and_Biomedical_Entities_Related_to_COVID-19_Automated_Computational_Approach/25958059CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/259580592020-11-10T09:00:00Z |
| spellingShingle | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach Junaed Younus Khan (16870110) Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Cardiovascular medicine and haematology Oncology and carcinogenesis Information and computing sciences Artificial intelligence Software engineering COVID-19 2019-nCoV coronavirus SARS-CoV-2 SARS remdesivir statin statins dexamethasone ivermectin hydroxychloroquine |
| status_str | publishedVersion |
| title | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach |
| title_full | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach |
| title_fullStr | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach |
| title_full_unstemmed | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach |
| title_short | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach |
| title_sort | Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach |
| topic | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Cardiovascular medicine and haematology Oncology and carcinogenesis Information and computing sciences Artificial intelligence Software engineering COVID-19 2019-nCoV coronavirus SARS-CoV-2 SARS remdesivir statin statins dexamethasone ivermectin hydroxychloroquine |