Neuro-symbolic representation learning on biological knowledge graphs

Handle URI:
http://hdl.handle.net/10754/623293
Title:
Neuro-symbolic representation learning on biological knowledge graphs
Authors:
Alshahrani, Mona ( 0000-0002-9848-8248 ) ; Khan, Mohammed Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Núria; Hoehndorf, Robert ( 0000-0001-8149-5890 )
Abstract:
Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge.We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of SemanticWeb based knowledge bases in biology to use in machine learning and data analytics.https://github.com/bio-ontology-research-group/walking-rdf-and-owl.robert.hoehndorf@kaust.edu.sa.Supplementary data are available at Bioinformatics online.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Alshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, et al. (2017) Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx275.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
21-Apr-2017
DOI:
10.1093/bioinformatics/btx275
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
This work was supported by funding from King Abdullah University of Science and Technology (KAUST).
Additional Links:
https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx275
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlshahrani, Monaen
dc.contributor.authorKhan, Mohammed Asifen
dc.contributor.authorMaddouri, Omaren
dc.contributor.authorKinjo, Akira Ren
dc.contributor.authorQueralt-Rosinach, Núriaen
dc.contributor.authorHoehndorf, Roberten
dc.date.accessioned2017-04-30T10:17:00Z-
dc.date.available2017-04-30T10:17:00Z-
dc.date.issued2017-04-21en
dc.identifier.citationAlshahrani M, Khan MA, Maddouri O, Kinjo AR, Queralt-Rosinach N, et al. (2017) Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx275.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.doi10.1093/bioinformatics/btx275en
dc.identifier.urihttp://hdl.handle.net/10754/623293-
dc.description.abstractBiological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge.We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of SemanticWeb based knowledge bases in biology to use in machine learning and data analytics.https://github.com/bio-ontology-research-group/walking-rdf-and-owl.robert.hoehndorf@kaust.edu.sa.Supplementary data are available at Bioinformatics online.en
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST).en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx275en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleNeuro-symbolic representation learning on biological knowledge graphsen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionLife Sciences Division, College of Science & Engineering, Hamad Bin Khalifa University, HBKU, PO Box 5825, Doha, Qatar.en
dc.contributor.institutionInstitute for Protein Research, Osaka University 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.en
dc.contributor.institutionDepartment of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA.en
kaust.authorAlshahrani, Monaen
kaust.authorKhan, Mohammed Asifen
kaust.authorHoehndorf, Roberten
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