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dc.contributor.authorThafar, Maha A.
dc.contributor.authorOlayan, Rawan S.
dc.contributor.authorAlbaradei, Somayah
dc.contributor.authorBajic, Vladimir B.
dc.contributor.authorGojobori, Takashi
dc.contributor.authorEssack, Magbubah
dc.contributor.authorGao, Xin
dc.date.accessioned2021-09-27T08:24:48Z
dc.date.available2021-09-27T08:24:48Z
dc.date.issued2021-09-22
dc.date.submitted2020-12-24
dc.identifier.citationThafar, M. A., Olayan, R. S., Albaradei, S., Bajic, V. B., Gojobori, T., Essack, M., & Gao, X. (2021). DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning. Journal of Cheminformatics, 13(1). doi:10.1186/s13321-021-00552-w
dc.identifier.issn1758-2946
dc.identifier.pmid34551818
dc.identifier.doi10.1186/s13321-021-00552-w
dc.identifier.urihttp://hdl.handle.net/10754/671958
dc.description.abstractAbstractDrug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug–target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.
dc.description.sponsorshipThe research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) through the Awards Nos. BAS/1/1606-01-01, BAS/1/1059-01-01, BAS/1/1624-01-01, FCC/1/1976-20-01, and FCC/1/1976-26-01.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00552-w
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectRandom Walk
dc.subjectHeterogeneous Network
dc.subjectRepresentation Learning
dc.subjectDrug Repositioning
dc.subjectCheminformatics
dc.subjectEnsemble Learning
dc.subjectLink Prediction
dc.subjectDrug–target Interaction
dc.subjectNetwork Embedding
dc.titleDTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentBiological and Environmental Science and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalJournal of Cheminformatics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCollege of Computers and Information Technology, Computer Science Department, Taif University, Taif, Kingdom of Saudi Arabia.
dc.contributor.institutionThe Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
dc.contributor.institutionFaculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.
dc.identifier.volume13
dc.identifier.issue1
kaust.personThafar, Maha A.
kaust.personAlbaradei, Somayah
kaust.personBajic, Vladimir B.
kaust.personGojobori, Takashi
kaust.personEssack, Magbubah
kaust.personGao, Xin
kaust.grant.numberBAS/1/1606-01-01
kaust.grant.numberBAS/1/1059-01-01
kaust.grant.numberFCC/1/1976-20-01
kaust.grant.numberFCC/1/1976-26-01
dc.date.accepted2021-09-05
refterms.dateFOA2021-09-27T08:27:29Z
kaust.acknowledged.supportUnitBAS
kaust.acknowledged.supportUnitFCC
dc.date.published-online2021-09-22
dc.date.published-print2021-12


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.