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dc.contributor.authorHan, Min
dc.contributor.authorLiang, Jun
dc.contributor.authorJin, Biao
dc.contributor.authorWang, Ziwei
dc.contributor.authorWu, Wanlu
dc.contributor.authorArp, Hans Peter Heinrich
dc.date.accessioned2024-07-12T07:32:45Z
dc.date.available2024-07-12T07:32:45Z
dc.date.created2024-02-26T09:45:00Z
dc.date.issued2024
dc.identifier.citationiScience. 2024, 27 (2), 1-17.en_US
dc.identifier.issn2589-0042
dc.identifier.urihttps://hdl.handle.net/11250/3140468
dc.description.abstractVarious synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) substances have been recognized as a threat to drinking water resources. It has not yet been assessed how many COVID-19 related substances could be considered PMT substances. One reason is the lack of high-quality experimental data for the identification of PMT substances. To solve this problem, we applied a machine learning model to identify the PMT substances among COVID-19 related chemicals. The optimal model achieved an accuracy of 90.6% based on external test data. The model interpretation and causal inference indicated that our approach understood causation between PMT properties and molecular descriptors. Notably, the screening results showed that over 60% of the COVID-19 chemicals considered are candidate PMT substances, which should be prioritized to prevent undue pollution of water resources.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleMachine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking wateren_US
dc.title.alternativeMachine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking wateren_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1-17en_US
dc.source.volume27en_US
dc.source.journaliScienceen_US
dc.source.issue2en_US
dc.identifier.doi10.1016/j.isci.2024.109012
dc.identifier.cristin2249635
dc.relation.projectEC/H2020/101036756en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal