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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.accessioned2020-09-07T08:01:37Z
dc.date.available2020-09-07T08:01:37Z
dc.date.issued2020
dc.identifier.citationThafar, M. A., Olayan, R. S., Ashoor, H., Somayah Albaradei, Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020). DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques. figshare. https://doi.org/10.6084/M9.FIGSHARE.C.5044301
dc.identifier.doi10.6084/m9.figshare.c.5044301
dc.identifier.urihttp://hdl.handle.net/10754/664969
dc.description.abstractAbstract In 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.publisherfigshare
dc.subjectArtificial Intelligence and Image Processing
dc.subjectFOS: Computer and information sciences
dc.titleDTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
dc.typeDataset
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentBiological and Environmental Sciences 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 Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
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.
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
dc.relation.issupplementtoDOI:10.1186/s13321-020-00447-2
display.relations<b> Is Supplement To:</b><br/> <ul> <li><i>[Article]</i> <br/> Thafar, 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. DOI: <a href="https://doi.org/10.1186/s13321-020-00447-2" >10.1186/s13321-020-00447-2</a> HANDLE: <a href="http://hdl.handle.net/10754/663987">10754/663987</a></li></ul>


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