Neuro-symbolic representation learning on biological knowledge graphs
<h3>Motivation</h3><p dir="ltr">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 gra...
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2017
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| _version_ | 1864513557009268736 |
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| author | Mona Alshahrani (3576653) |
| author2 | Mohammad Asif Khan (9220790) Omar Maddouri (19725331) Akira R Kinjo (19725334) Núria Queralt-Rosinach (3846793) Robert Hoehndorf (38206) |
| author2_role | author author author author author |
| author_facet | Mona Alshahrani (3576653) Mohammad Asif Khan (9220790) Omar Maddouri (19725331) Akira R Kinjo (19725334) Núria Queralt-Rosinach (3846793) Robert Hoehndorf (38206) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mona Alshahrani (3576653) Mohammad Asif Khan (9220790) Omar Maddouri (19725331) Akira R Kinjo (19725334) Núria Queralt-Rosinach (3846793) Robert Hoehndorf (38206) |
| dc.date.none.fl_str_mv | 2017-04-25T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1093/bioinformatics/btx275 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Neuro-symbolic_representation_learning_on_biological_knowledge_graphs/27087976 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Knowledge graphs Data integration Feature learning methods Symbolic methods Knowledge representation Neural networks Node embeddings |
| dc.title.none.fl_str_mv | Neuro-symbolic representation learning on biological knowledge graphs |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Motivation</h3><p dir="ltr">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.</p><h3>Availability and implementation</h3><p dir="ltr">https://github.com/bio-ontology-research-group/walking-rdf-and-owl</p><h3>Supplementary information</h3><p dir="ltr">Supplementary data are available at Bioinformatics online.</p><h2>Other Information</h2><p dir="ltr">Published in: Bioinformatics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1093/bioinformatics/btx275" target="_blank">https://dx.doi.org/10.1093/bioinformatics/btx275</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d9ffc9141dae446e7b56c1734994af72 |
| identifier_str_mv | 10.1093/bioinformatics/btx275 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27087976 |
| publishDate | 2017 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Neuro-symbolic representation learning on biological knowledge graphsMona Alshahrani (3576653)Mohammad Asif Khan (9220790)Omar Maddouri (19725331)Akira R Kinjo (19725334)Núria Queralt-Rosinach (3846793)Robert Hoehndorf (38206)Biological sciencesBioinformatics and computational biologyKnowledge graphsData integrationFeature learning methodsSymbolic methodsKnowledge representationNeural networksNode embeddings<h3>Motivation</h3><p dir="ltr">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.</p><h3>Availability and implementation</h3><p dir="ltr">https://github.com/bio-ontology-research-group/walking-rdf-and-owl</p><h3>Supplementary information</h3><p dir="ltr">Supplementary data are available at Bioinformatics online.</p><h2>Other Information</h2><p dir="ltr">Published in: Bioinformatics<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1093/bioinformatics/btx275" target="_blank">https://dx.doi.org/10.1093/bioinformatics/btx275</a></p>2017-04-25T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1093/bioinformatics/btx275https://figshare.com/articles/journal_contribution/Neuro-symbolic_representation_learning_on_biological_knowledge_graphs/27087976CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270879762017-04-25T03:00:00Z |
| spellingShingle | Neuro-symbolic representation learning on biological knowledge graphs Mona Alshahrani (3576653) Biological sciences Bioinformatics and computational biology Knowledge graphs Data integration Feature learning methods Symbolic methods Knowledge representation Neural networks Node embeddings |
| status_str | publishedVersion |
| title | Neuro-symbolic representation learning on biological knowledge graphs |
| title_full | Neuro-symbolic representation learning on biological knowledge graphs |
| title_fullStr | Neuro-symbolic representation learning on biological knowledge graphs |
| title_full_unstemmed | Neuro-symbolic representation learning on biological knowledge graphs |
| title_short | Neuro-symbolic representation learning on biological knowledge graphs |
| title_sort | Neuro-symbolic representation learning on biological knowledge graphs |
| topic | Biological sciences Bioinformatics and computational biology Knowledge graphs Data integration Feature learning methods Symbolic methods Knowledge representation Neural networks Node embeddings |