About: Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations

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About: Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
Creator	Shen, Li Lee, Brian Lee, Seung Park, Ji Verma, Anurag Chen, Ziqi Kim, Dokyoon Nam, Yonghyun Ning, Xia Yun, Jae-Seung
Source	MedRxiv
abstract	Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19.
has issue date	2020-08-14(xsd:dateTime)
bibo:doi	10.1101/2020.08.11.20173120
has license	medrxiv
sha1sum (hex)	f41f11971b3e54eb32209a51852c9c6aa08b2102
schema:url	https://doi.org/10.1101/2020.08.11.20173120
resource representing a document's title	Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
resource representing a document's body	covid:f41f11971b3e54eb32209a51852c9c6aa08b2102#body_text
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