Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water
dc.contributor.author | Han, Min | |
dc.contributor.author | Liang, Jun | |
dc.contributor.author | Jin, Biao | |
dc.contributor.author | Wang, Ziwei | |
dc.contributor.author | Wu, Wanlu | |
dc.contributor.author | Arp, Hans Peter Heinrich | |
dc.date.accessioned | 2024-07-12T08:44:47Z | |
dc.date.available | 2024-07-12T08:44:47Z | |
dc.date.created | 2024-02-26T09:45:00Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | iScience. 2024, 27 (2), . | |
dc.identifier.issn | 2589-0042 | |
dc.identifier.uri | https://hdl.handle.net/11250/3140528 | |
dc.language.iso | eng | |
dc.title | Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water | |
dc.title.alternative | Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water | |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.description.version | publishedVersion | |
dc.source.pagenumber | 0 | |
dc.source.volume | 27 | |
dc.source.journal | iScience | |
dc.source.issue | 2 | |
dc.identifier.doi | 10.1016/j.isci.2024.109012 | |
dc.identifier.cristin | 2249635 | |
dc.relation.project | EC/H2020/101036756 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 |
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