Neuro-symbolic representation learning on biological knowledge graphs
dc.contributor.author | AlShahrani, Mona | |
dc.contributor.author | Khan, Ameer Mohammed Asif | |
dc.contributor.author | Maddouri, Omar | |
dc.contributor.author | Kinjo, Akira R | |
dc.contributor.author | Queralt-Rosinach, Núria | |
dc.contributor.author | Hoehndorf, Robert | |
dc.date.accessioned | 2017-04-30T10:17:00Z | |
dc.date.available | 2017-04-30T10:17:00Z | |
dc.date.issued | 2017-04-25 | |
dc.identifier.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. | |
dc.identifier.issn | 1367-4803 | |
dc.identifier.issn | 1460-2059 | |
dc.identifier.doi | 10.1093/bioinformatics/btx275 | |
dc.identifier.uri | http://hdl.handle.net/10754/623293 | |
dc.description.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. | |
dc.description.sponsorship | This work was supported by funding from King Abdullah University of Science and Technology (KAUST). | |
dc.publisher | Oxford University Press (OUP) | |
dc.relation.url | https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx275 | |
dc.rights | This 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. | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.title | Neuro-symbolic representation learning on biological knowledge graphs | |
dc.type | Article | |
dc.contributor.department | Bio-Ontology Research Group (BORG) | |
dc.contributor.department | Computational Bioscience Research Center (CBRC) | |
dc.contributor.department | Computer Science Program | |
dc.contributor.department | Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division | |
dc.identifier.journal | Bioinformatics | |
dc.eprint.version | Publisher's Version/PDF | |
dc.contributor.institution | Life Sciences Division, College of Science & Engineering, Hamad Bin Khalifa University, HBKU, PO Box 5825, Doha, Qatar. | |
dc.contributor.institution | Institute for Protein Research, Osaka University 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. | |
dc.contributor.institution | Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037, USA. | |
dc.identifier.arxivid | 1612.04256 | |
kaust.person | Alshahrani, Mona | |
kaust.person | Khan, Ameer Mohammed Asif | |
kaust.person | Hoehndorf, Robert | |
dc.relation.issupplementedby | github:bio-ontology-research-group/walking-rdf-and-owl | |
refterms.dateFOA | 2018-06-13T13:18:08Z | |
display.relations | <b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: bio-ontology-research-group/walking-rdf-and-owl: Feature learning over RDF data and OWL ontologies. Publication Date: 2016-06-14. github: <a href="https://github.com/bio-ontology-research-group/walking-rdf-and-owl" >bio-ontology-research-group/walking-rdf-and-owl</a> Handle: <a href="http://hdl.handle.net/10754/667036" >10754/667036</a></a></li></ul> | |
dc.date.published-online | 2017-04-25 | |
dc.date.published-print | 2017-09-01 |
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