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dc.contributor.authorHinnerichs, Tilman
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2021-08-04T13:25:15Z
dc.date.available2021-05-03T07:56:07Z
dc.date.available2021-08-04T13:25:15Z
dc.date.issued2021-07-28
dc.date.submitted2021-04-28
dc.identifier.citationHinnerichs, T., & Hoehndorf, R. (2021). DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions. Bioinformatics. doi:10.1093/bioinformatics/btab548
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.pmid34320178
dc.identifier.doi10.1093/bioinformatics/btab548
dc.identifier.urihttp://hdl.handle.net/10754/669064
dc.description.abstractMotivation In silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks. Results We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3790-01-01 and URF/1/4355-01-01.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab548/6329632
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.eprint.versionPublisher's Version/PDF
kaust.personHinnerichs, Tilman
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/3790-01-01 and URF/1/4355-01-01
dc.date.accepted2021-07-26
dc.relation.issupplementedbygithub:THinnerichs/DTI-VOODOO
refterms.dateFOA2021-05-03T07:57:09Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: THinnerichs/DTI-VOODOO: A PPI network approach driven approach to drug-target-interaction prediction using deep graph learning methods.. Publication Date: 2019-11-05. github: <a href="https://github.com/THinnerichs/DTI-VOODOO" >THinnerichs/DTI-VOODOO</a> Handle: <a href="http://hdl.handle.net/10754/669107" >10754/669107</a></a></li></ul>
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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