Details

Title
Author Correction: DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets
Author
Raies, Arwa 1   VIAFID ORCID Logo  ; Tulodziecka, Ewa 1 ; Stainer, James 1 ; Middleton, Lawrence 1 ; Dhindsa, Ryan S. 2   VIAFID ORCID Logo  ; Hill, Pamela 3 ; Engkvist, Ola 4   VIAFID ORCID Logo  ; Harper, Andrew R. 1 ; Petrovski, Slavé 5 ; Vitsios, Dimitrios 1   VIAFID ORCID Logo 

 Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK (GRID:grid.417815.e) (ISNI:0000 0004 5929 4381) 
 Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, USA (GRID:grid.418152.b) (ISNI:0000 0004 0543 9493); Baylor College of Medicine, Department of Molecular and Human Genetics, Houston, USA (GRID:grid.39382.33) (ISNI:0000 0001 2160 926X) 
 Emerging Innovations, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, USA (GRID:grid.418152.b) (ISNI:0000 0004 0543 9493) 
 Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden (GRID:grid.418151.8) (ISNI:0000 0001 1519 6403) 
 Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK (GRID:grid.417815.e) (ISNI:0000 0004 5929 4381); University of Melbourne, Austin Health, Department of Medicine, Melbourne, Australia (GRID:grid.1008.9) (ISNI:0000 0001 2179 088X) 
Pages
710
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
23993642
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2835689041
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.