Show simple item record

dc.contributor.authorThafar, Maha A.
dc.contributor.authorOlayan, Rawan S.
dc.contributor.authorAshoor, Haitham
dc.contributor.authorAlbaradei, Somayah
dc.contributor.authorBajic, Vladimir B.
dc.contributor.authorGao, Xin
dc.contributor.authorGojobori, Takashi
dc.contributor.authorEssack, Magbubah
dc.date.accessioned2020-07-05T08:06:38Z
dc.date.available2020-07-05T08:06:38Z
dc.date.issued2020-07-02
dc.date.submitted2019-12-10
dc.identifier.citationThafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., … Essack, M. (2020). DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques. Journal of Cheminformatics, 12(1). doi:10.1186/s13321-020-00447-2
dc.identifier.issn1758-2946
dc.identifier.doi10.1186/s13321-020-00447-2
dc.identifier.urihttp://hdl.handle.net/10754/663987
dc.description.abstractIn silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.
dc.description.sponsorshipThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST).
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/1/1976-17-01, and FCC/1/1976-26-01.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00447-2
dc.relation.urlhttps://jcheminf.biomedcentral.com/track/pdf/10.1186/s13321-020-00447-2
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://creativeco mmons.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.titleDTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques.
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
dc.contributor.departmentComputer Science Program
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.identifier.journalJournal of Cheminformatics
dc.identifier.pmcidPMC7325230
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCollage of Computers and Information Technology, 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.volume12
dc.identifier.issue1
kaust.personThafar, Maha A.
kaust.personOlayan, Rawan S.
kaust.personAshoor, Haitham
kaust.personAlbaradei, Somayah
kaust.personBajic, Vladimir B.
kaust.personGao, Xin
kaust.personGojobori, Takashi
kaust.personGojobori, Takashi
kaust.personEssack, Magbubah
kaust.grant.numberBAS/1/1606-01-01
kaust.grant.numberBAS/1/1059-01-01
kaust.grant.numberBAS/1/1624-01-01
kaust.grant.numberFCC/1/1976-17-01
kaust.grant.numberFCC/1/1976-26-01.
dc.date.accepted2020-06-16
refterms.dateFOA2020-07-05T08:07:51Z
display.relations<b> Is Supplemented By:</b> <br/> <ul> <li><i>[Dataset]</i> <br/> . DOI: <a href="https://doi.org/10.6084/m9.figshare.c.5044301">10.6084/m9.figshare.c.5044301</a> HANDLE: <a href="http://hdl.handle.net/10754/664969">10754/664969</a></li></ul>


Files in this item

Thumbnail
Name:
Articlefile1.pdf
Size:
1.818Mb
Format:
PDF
Description:
Publisher's Version/PDF

This item appears in the following Collection(s)

Show simple item record

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://creativeco
mmons.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://creativeco mmons.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.