DC Field | Value | Language |
---|---|---|
dc.contributor.author | 류가현 | ko |
dc.contributor.author | 이상엽 | ko |
dc.contributor.author | 류재용 | ko |
dc.contributor.author | 김현욱 | ko |
dc.date.accessioned | 2022-09-19T06:01:09Z | - |
dc.date.available | 2022-09-19T06:01:09Z | - |
dc.date.created | 2022-09-15 | - |
dc.date.issued | 2022-04-15 | - |
dc.identifier.citation | 2022 한국생물공학회 춘계학술발표대회 및 국제심포지엄 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298590 | - |
dc.language | English | - |
dc.publisher | 한국생물공학회 | - |
dc.title | Deep learning approach for high-quality and high-throughput enzyme commission number prediction | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 2022 한국생물공학회 춘계학술발표대회 및 국제심포지엄 | - |
dc.identifier.conferencecountry | KO | - |
dc.identifier.conferencelocation | 대전컨벤션센터 | - |
dc.contributor.localauthor | 이상엽 | - |
dc.contributor.nonIdAuthor | 류가현 | - |
dc.contributor.nonIdAuthor | 류재용 | - |
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