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Using informative f...
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Aghdam, RosaInst Res Fundamental Sci IPM, Sch Biol Sci, Tehran, Iran.
(författare)
Using informative features in machine learning based method for COVID-19 drug repurposing
- Artikel/kapitelEngelska2021
Förlag, utgivningsår, omfång ...
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2021-09-20
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Springer Nature,2021
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printrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:kth-303049
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303049URI
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https://doi.org/10.1186/s13321-021-00553-9DOI
Kompletterande språkuppgifter
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Språk:engelska
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Sammanfattning på:engelska
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Klassifikation
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:art swepub-publicationtype
Anmärkningar
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QC 20211006
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Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.
Ämnesord och genrebeteckningar
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Coronavirus disease 2019
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SARS-CoV-2
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Protein-protein interaction
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Clustering method
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Habibi, MahnazIslamic Azad Univ, Dept Math, Qazvin Branch, Qazvin, Iran.
(författare)
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Taheri, GolnazKTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab(Swepub:kth)u1cq22h1
(författare)
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Inst Res Fundamental Sci IPM, Sch Biol Sci, Tehran, Iran.Islamic Azad Univ, Dept Math, Qazvin Branch, Qazvin, Iran.
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Journal of Cheminformatics: Springer Nature13:11758-2946
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