Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/33343
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dc.contributor.authorRaies, Arwa-
dc.contributor.authorTulodziecka, Ewa-
dc.contributor.authorStainer, James-
dc.contributor.authorMiddleton, Lawrence-
dc.contributor.authorDhindsa, Ryan S-
dc.contributor.authorHill, Pamela-
dc.contributor.authorEngkvist, Ola-
dc.contributor.authorHarper, Andrew R-
dc.contributor.authorPetrovski, Slavé-
dc.contributor.authorVitsios, Dimitrios-
dc.date2023-
dc.date.accessioned2023-07-19T02:15:37Z-
dc.date.available2023-07-19T02:15:37Z-
dc.date.issued2023-07-11-
dc.identifier.citationCommunications Biology 2023-07-11; 6(1)en_US
dc.identifier.issn2399-3642-
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/33343-
dc.language.isoeng-
dc.titleAuthor Correction: DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.en_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleCommunications Biologyen_US
dc.identifier.affiliationCentre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.en_US
dc.identifier.affiliationEmerging Innovations, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, MA, USA.en_US
dc.identifier.affiliationMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.en_US
dc.identifier.affiliationMedicine (University of Melbourne)en_US
dc.identifier.doi10.1038/s42003-023-05086-5en_US
dc.type.contentTexten_US
dc.identifier.orcid0000-0003-3952-7363en_US
dc.identifier.orcid0000-0002-8965-0813en_US
dc.identifier.orcid0000-0003-4970-6461en_US
dc.identifier.orcid0000-0002-8939-5445en_US
dc.identifier.pubmedid37433831-
dc.description.volume6-
dc.description.issue1-
dc.description.startpage710-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
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