AI3SD Video: Towards biological plausibility using linked open data
AI3SD Video: Towards biological plausibility using linked open data
Behind risk assessment is experimental evidence. Behind biological knowledge is primary literature. However, because the amount of knowledge keeps growing, our experimental technologies are advancing and getting increasingly complex, even experts can no longer keep up with the progress in mechanistic understanding, outside their increasingly specialistic domain. At the same time, the number of biological questions with a simple answer keeps dropping and many modern questions have complex answers. Access to the right facts at the right time needs a change of thinking. The idea of linking facts and data at a large scale was envisioned long ago, but only recently became viable, with the introduction of the semantic web and linked open data. These new technologies make it possible to easily link remote knowledge, taking advantage of globally unique identifiers and exact meaning with ontologies [1,2]. This presentation outlines how we applied these ideas to the life sciences in general and with applications to toxicology. Using eNanoMapper [3], WikiPathways [4], and Wikidata [5], it will show how semantic web approaches can be used to answer questions that are much harder to answer with older approaches. Examples will show 1. how we can use SPARQL to return all assay experiments for all types of metal oxides, 2. how biological pathway knowledge can be combined with knowledge from chemical databases, and 3. how we can find research about and scholars that study particular genes, proteins, or toxicants.
References:
Samwald, M.et al. Linked open drug data for pharmaceutical research and development. Journal of Cheminformatics 3, 19 (2011)
Willighagen, E.L. et al. The ChEMBL database as linked open data. Journal of Cheminformatics 5, 23 (2013)
Hastings, J. et al. eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment. Journal of Biomedical Semantics 6, (2015)
Waagmeester, A. et al. Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources. PLOS Comp Biology 12, e1004989 (2016)
Waagmeester, A. et al. Wikidata as a knowledge graph for the life sciences. eLife 9, e52614 (2020).
AI, AI3SD Event, Artificial Intelligence, Data Science, Ontologies, OWL, RDF, Research, Research Data Management, Responsible Research, Semantic Web, Semantics, SPARQL
Willighagen, Egon L.
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
13 October 2021
Willighagen, Egon L.
0ff0c32b-04d1-4f67-aa4f-823bd9e92c47
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Willighagen, Egon L.
(2021)
AI3SD Video: Towards biological plausibility using linked open data.
Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan
(eds.)
AI3SD Autumn Seminar Series 2021.
13 Oct - 15 Dec 2021.
(doi:10.5258/SOTON/AI3SD0155).
Record type:
Conference or Workshop Item
(Other)
Abstract
Behind risk assessment is experimental evidence. Behind biological knowledge is primary literature. However, because the amount of knowledge keeps growing, our experimental technologies are advancing and getting increasingly complex, even experts can no longer keep up with the progress in mechanistic understanding, outside their increasingly specialistic domain. At the same time, the number of biological questions with a simple answer keeps dropping and many modern questions have complex answers. Access to the right facts at the right time needs a change of thinking. The idea of linking facts and data at a large scale was envisioned long ago, but only recently became viable, with the introduction of the semantic web and linked open data. These new technologies make it possible to easily link remote knowledge, taking advantage of globally unique identifiers and exact meaning with ontologies [1,2]. This presentation outlines how we applied these ideas to the life sciences in general and with applications to toxicology. Using eNanoMapper [3], WikiPathways [4], and Wikidata [5], it will show how semantic web approaches can be used to answer questions that are much harder to answer with older approaches. Examples will show 1. how we can use SPARQL to return all assay experiments for all types of metal oxides, 2. how biological pathway knowledge can be combined with knowledge from chemical databases, and 3. how we can find research about and scholars that study particular genes, proteins, or toxicants.
References:
Samwald, M.et al. Linked open drug data for pharmaceutical research and development. Journal of Cheminformatics 3, 19 (2011)
Willighagen, E.L. et al. The ChEMBL database as linked open data. Journal of Cheminformatics 5, 23 (2013)
Hastings, J. et al. eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment. Journal of Biomedical Semantics 6, (2015)
Waagmeester, A. et al. Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources. PLOS Comp Biology 12, e1004989 (2016)
Waagmeester, A. et al. Wikidata as a knowledge graph for the life sciences. eLife 9, e52614 (2020).
Video
AI3SDAutumnSeminar-131021-EgonWillighagen
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More information
Published date: 13 October 2021
Additional Information:
I study the role of machine representation of knowledge and hypothesis in life sciences, metabolomics, drug discovery, and toxicology, involving cheminformatics, chemometrics and semantic web technologies. In the past, I have applied research on this also to QSAR and crystallography. Open source programming and Open science is also my main hobby, resulting in participation in, amongst many others, Chemistry Development Kit, WikiPathways, Bioclipse, BridgeDb, and others.
Venue - Dates:
AI3SD Autumn Seminar Series 2021, 2021-10-13 - 2021-12-15
Keywords:
AI, AI3SD Event, Artificial Intelligence, Data Science, Ontologies, OWL, RDF, Research, Research Data Management, Responsible Research, Semantic Web, Semantics, SPARQL
Identifiers
Local EPrints ID: 451924
URI: http://eprints.soton.ac.uk/id/eprint/451924
PURE UUID: 59f340a3-4768-436b-9fc4-ff8181462880
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Date deposited: 03 Nov 2021 17:41
Last modified: 17 Mar 2024 03:51
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Contributors
Author:
Egon L. Willighagen
Editor:
Mahesan Niranjan
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