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Bioinformatics. 2017 Sep 1;33(17):2723-2730. doi: 10.1093/bioinformatics/btx275.

Neuro-symbolic representation learning on biological knowledge graphs.

Author information

1
Computer, Electrical and Mathematical Sciences & Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.
2
Life Sciences Division, College of Science & Engineering, Hamad Bin Khalifa University, HBKU, Doha, Qatar.
3
Institute for Protein Research, Osaka University 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
4
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA.

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.

PMID:
28449114
PMCID:
PMC5860058
DOI:
10.1093/bioinformatics/btx275
[Indexed for MEDLINE]
Free PMC Article

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