Heterogeneous network-based drug repurposing for COVID-19
The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses (HCoVs), which spreads rapidly around the world. Compared with new drug development, drug repurposing may be the best shortcut for treating COVID-19. Therefore, we constructed a comprehensive heterogeneous network based on the H...
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| creator | Jin, Shuting Zeng, Xiangxiang Huang, Wei Xia, Feng Jiang, Changzhi Liu, Xiangrong Peng, Shaoliang |
| description | The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses
(HCoVs), which spreads rapidly around the world. Compared with new drug
development, drug repurposing may be the best shortcut for treating COVID-19.
Therefore, we constructed a comprehensive heterogeneous network based on the
HCoVs-related target proteins and use the previously proposed deepDTnet, to
discover potential drug candidates for COVID-19. We obtain high performance in
predicting the possible drugs effective for COVID-19 related proteins. In
summary, this work utilizes a powerful heterogeneous network-based deep
learning method, which may be beneficial to quickly identify candidate
repurposable drugs toward future clinical trials for COVID-19. The code and
data are available at https://github.com/stjin-XMU/HnDR-COVID. |
| doi_str_mv | 10.48550/arxiv.2107.09217 |
| format | Article |
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(HCoVs), which spreads rapidly around the world. Compared with new drug
development, drug repurposing may be the best shortcut for treating COVID-19.
Therefore, we constructed a comprehensive heterogeneous network based on the
HCoVs-related target proteins and use the previously proposed deepDTnet, to
discover potential drug candidates for COVID-19. We obtain high performance in
predicting the possible drugs effective for COVID-19 related proteins. In
summary, this work utilizes a powerful heterogeneous network-based deep
learning method, which may be beneficial to quickly identify candidate
repurposable drugs toward future clinical trials for COVID-19. The code and
data are available at https://github.com/stjin-XMU/HnDR-COVID.</description><identifier>DOI: 10.48550/arxiv.2107.09217</identifier><language>eng</language><subject>Computer Science - Computers and Society ; Computer Science - Learning</subject><creationdate>2021-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2107.09217$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2107.09217$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Jin, Shuting</creatorcontrib><creatorcontrib>Zeng, Xiangxiang</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>Xia, Feng</creatorcontrib><creatorcontrib>Jiang, Changzhi</creatorcontrib><creatorcontrib>Liu, Xiangrong</creatorcontrib><creatorcontrib>Peng, Shaoliang</creatorcontrib><title>Heterogeneous network-based drug repurposing for COVID-19</title><description>The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses
(HCoVs), which spreads rapidly around the world. Compared with new drug
development, drug repurposing may be the best shortcut for treating COVID-19.
Therefore, we constructed a comprehensive heterogeneous network based on the
HCoVs-related target proteins and use the previously proposed deepDTnet, to
discover potential drug candidates for COVID-19. We obtain high performance in
predicting the possible drugs effective for COVID-19 related proteins. In
summary, this work utilizes a powerful heterogeneous network-based deep
learning method, which may be beneficial to quickly identify candidate
repurposable drugs toward future clinical trials for COVID-19. The code and
data are available at https://github.com/stjin-XMU/HnDR-COVID.</description><subject>Computer Science - Computers and Society</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7tOwzAUQL0woMIHMOEfcOob27E9VinQSpW6VKyRE19HETSObhoef48oTGc7OoexB5CFdsbIdaCv4aMoQdpC-hLsLfM7vCDlHkfMy8xHvHxmehNtmDHySEvPCaeFpjwPY89TJl4fX_dbAf6O3aTwPuP9P1fs9Px0qnficHzZ15uDCJW1QldBe0SIICvVpuA6dKaVAKqUVvmkndPRGFCIygSIrjNll9C66FznE6oVe_zTXtubiYZzoO_m96G5PqgftV5Axg</recordid><startdate>20210719</startdate><enddate>20210719</enddate><creator>Jin, Shuting</creator><creator>Zeng, Xiangxiang</creator><creator>Huang, Wei</creator><creator>Xia, Feng</creator><creator>Jiang, Changzhi</creator><creator>Liu, Xiangrong</creator><creator>Peng, Shaoliang</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20210719</creationdate><title>Heterogeneous network-based drug repurposing for COVID-19</title><author>Jin, Shuting ; Zeng, Xiangxiang ; Huang, Wei ; Xia, Feng ; Jiang, Changzhi ; Liu, Xiangrong ; Peng, Shaoliang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-46a49ee1d1063bfa8ce85b011320739f4884d5513ee35a1d8c52cfe78d88c9fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computer Science - Computers and Society</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Jin, Shuting</creatorcontrib><creatorcontrib>Zeng, Xiangxiang</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>Xia, Feng</creatorcontrib><creatorcontrib>Jiang, Changzhi</creatorcontrib><creatorcontrib>Liu, Xiangrong</creatorcontrib><creatorcontrib>Peng, Shaoliang</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jin, Shuting</au><au>Zeng, Xiangxiang</au><au>Huang, Wei</au><au>Xia, Feng</au><au>Jiang, Changzhi</au><au>Liu, Xiangrong</au><au>Peng, Shaoliang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Heterogeneous network-based drug repurposing for COVID-19</atitle><date>2021-07-19</date><risdate>2021</risdate><abstract>The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses
(HCoVs), which spreads rapidly around the world. Compared with new drug
development, drug repurposing may be the best shortcut for treating COVID-19.
Therefore, we constructed a comprehensive heterogeneous network based on the
HCoVs-related target proteins and use the previously proposed deepDTnet, to
discover potential drug candidates for COVID-19. We obtain high performance in
predicting the possible drugs effective for COVID-19 related proteins. In
summary, this work utilizes a powerful heterogeneous network-based deep
learning method, which may be beneficial to quickly identify candidate
repurposable drugs toward future clinical trials for COVID-19. The code and
data are available at https://github.com/stjin-XMU/HnDR-COVID.</abstract><doi>10.48550/arxiv.2107.09217</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computers and Society Computer Science - Learning |
| title | Heterogeneous network-based drug repurposing for COVID-19 |
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