Neuro-symbolic Representation Learning on Biological Knowledge Graphs

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2017 Apr 28

PMID 28449114

Citations 39

Authors

Mona Alshahrani

Mohammad Asif Khan

Omar Maddouri

Akira R Kinjo

Nuria Queralt-Rosinach

Robert Hoehndorf

Affiliations

Soon will be listed here.

Abstract

Motivation: 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. Results: 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 Semantic Web based knowledge bases in biology to use in machine learning and data analytics.

Availability And Implementation: https://github.com/bio-ontology-research-group/walking-rdf-and-owl.

Contact: robert.hoehndorf@kaust.edu.sa.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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References

Pinero J, Queralt-Rosinach N, Bravo A, Deu-Pons J, Bauer-Mehren A, Baron M . DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford). 2015; 2015:bav028. PMC: 4397996. DOI: 10.1093/database/bav028. View

Tatonetti N, Ye P, Daneshjou R, Altman R . Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012; 4(125):125ra31. PMC: 3382018. DOI: 10.1126/scitranslmed.3003377. View

Hoehndorf R, Dumontier M, Oellrich A, Wimalaratne S, Rebholz-Schuhmann D, Schofield P . A common layer of interoperability for biomedical ontologies based on OWL EL. Bioinformatics. 2011; 27(7):1001-8. PMC: 3065691. DOI: 10.1093/bioinformatics/btr058. View

Kuhn M, Campillos M, Letunic I, Jensen L, Bork P . A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol. 2010; 6:343. PMC: 2824526. DOI: 10.1038/msb.2009.98. View

Mungall C, Torniai C, Gkoutos G, Lewis S, Haendel M . Uberon, an integrative multi-species anatomy ontology. Genome Biol. 2012; 13(1):R5. PMC: 3334586. DOI: 10.1186/gb-2012-13-1-r5. View

Kohler S, Bauer S, Horn D, Robinson P . Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet. 2008; 82(4):949-58. PMC: 2427257. DOI: 10.1016/j.ajhg.2008.02.013. View

Wang W, Yang S, Zhang X, Li J . Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics. 2014; 30(20):2923-30. PMC: 4184255. DOI: 10.1093/bioinformatics/btu403. View

Kibbe W, Arze C, Felix V, Mitraka E, Bolton E, Fu G . Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. 2014; 43(Database issue):D1071-8. PMC: 4383880. DOI: 10.1093/nar/gku1011. View

Wilkinson M, Dumontier M, Aalbersberg I, Appleton G, Axton M, Baak A . The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016; 3:160018. PMC: 4792175. DOI: 10.1038/sdata.2016.18. View

10.

Hoehndorf R, Schofield P, Gkoutos G . Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Sci Rep. 2015; 5:10888. PMC: 4458913. DOI: 10.1038/srep10888. View

11.

Livingston K, Bada M, Baumgartner Jr W, Hunter L . KaBOB: ontology-based semantic integration of biomedical databases. BMC Bioinformatics. 2015; 16:126. PMC: 4448321. DOI: 10.1186/s12859-015-0559-3. View

12.

Kohler S, Doelken S, Mungall C, Bauer S, Firth H, Bailleul-Forestier I . The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 2013; 42(Database issue):D966-74. PMC: 3965098. DOI: 10.1093/nar/gkt1026. View

13.

Belleau F, Nolin M, Tourigny N, Rigault P, Morissette J . Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J Biomed Inform. 2008; 41(5):706-16. DOI: 10.1016/j.jbi.2008.03.004. View

14.

Katayama T, Wilkinson M, Aoki-Kinoshita K, Kawashima S, Yamamoto Y, Yamaguchi A . BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains. J Biomed Semantics. 2014; 5(1):5. PMC: 3978116. DOI: 10.1186/2041-1480-5-5. View

15.

Radivojac P, Clark W, Ronnen Oron T, Schnoes A, Wittkop T, Sokolov A . A large-scale evaluation of computational protein function prediction. Nat Methods. 2013; 10(3):221-7. PMC: 3584181. DOI: 10.1038/nmeth.2340. View

16.

Sokolov A, Funk C, Graim K, Verspoor K, Ben-Hur A . Combining heterogeneous data sources for accurate functional annotation of proteins. BMC Bioinformatics. 2013; 14 Suppl 3:S10. PMC: 3584846. DOI: 10.1186/1471-2105-14-S3-S10. View

17.

Hoehndorf R, Hiebert T, Hardy N, Schofield P, Gkoutos G, Dumontier M . Mouse model phenotypes provide information about human drug targets. Bioinformatics. 2013; 30(5):719-25. PMC: 3933875. DOI: 10.1093/bioinformatics/btt613. View

18.

Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W . The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007; 25(11):1251-5. PMC: 2814061. DOI: 10.1038/nbt1346. View

19.

Hoehndorf R, Schofield P, Gkoutos G . The role of ontologies in biological and biomedical research: a functional perspective. Brief Bioinform. 2015; 16(6):1069-80. PMC: 4652617. DOI: 10.1093/bib/bbv011. View

20.

Kuhn M, Szklarczyk D, Franceschini A, von Mering C, Jensen L, Bork P . STITCH 3: zooming in on protein-chemical interactions. Nucleic Acids Res. 2011; 40(Database issue):D876-80. PMC: 3245073. DOI: 10.1093/nar/gkr1011. View