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dc.contributor.authorAlshahrani, Mona
dc.contributor.authorAlmansour, Abdullah
dc.contributor.authorAlkhaldi, Asma
dc.contributor.authorThafar, Maha A.
dc.contributor.authorUludag, Mahmut
dc.contributor.authorEssack, Magbubah
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2022-04-18T13:35:27Z
dc.date.available2022-04-18T13:35:27Z
dc.date.issued2022-04-04
dc.identifier.citationAlshahrani, M., Almansour, A., Alkhaldi, A., Thafar, M. A., Uludag, M., Essack, M., & Hoehndorf, R. (2022). Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications. PeerJ, 10, e13061. Portico. https://doi.org/10.7717/peerj.13061
dc.identifier.issn2167-8359
dc.identifier.pmid35402106
dc.identifier.doi10.7717/peerj.13061
dc.identifier.urihttp://hdl.handle.net/10754/676306
dc.description.abstractBiomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.
dc.description.sponsorshipMona Alshahrani, Abdullah Almansour and Asma Alkhaldi are supported by the National Center of Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Saudi Arabia.
dc.description.sponsorshipMagbubah Essack has been supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) grant no. FCC/1/1976-20-01
dc.publisherPeerJ
dc.relation.urlhttps://peerj.com/articles/13061
dc.rightsArchived with thanks to PeerJ under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBiomedical Literature
dc.subjectLinked Data
dc.subjectBio-ontologies
dc.subjectMulti-modal Learning
dc.subjectDrug-target Interactions
dc.subjectBiomedical Knowledge Graphs
dc.subjectDrug-Indications
dc.titleCombining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalPeerJ
dc.identifier.pmcidPMC8988936
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionNational Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
dc.contributor.institutionCollege of Computers and Information Technology, Taif University, Taif, Saudi Arabia
dc.identifier.volume10
dc.identifier.pagese13061
kaust.personThafar, Maha A.
kaust.personUludag, Mahmut
kaust.personEssack, Magbubah
kaust.personHoehndorf, Robert
kaust.grant.numberFCC/1/1976-20-01
refterms.dateFOA2022-04-18T13:37:58Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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Archived with thanks to PeerJ under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as Archived with thanks to PeerJ under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/