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A new wave of innovation in semantic web tools for drug discovery

A new wave of innovation in semantic web tools for drug discovery
A new wave of innovation in semantic web tools for drug discovery
Introduction: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to dealing with the high levels of data for drug discovery. These technologies enable us to represent the data in a formal, structured, interoperable and comparable way, and to tease out undiscovered links between drug data (be it identifying new drug-targets or relevant compounds, or links between specific drugs and diseases).

Areas Covered: This review focuses on explaining how semantic web technologies are being used to aid advances in drug discovery. The main types of semantic web technologies are explained, outlining how they work and how they can be used in the drug discovery process, with a consideration of how the use of these technologies has progressed from their initial usage.

Expert Opinion: The increased availability of shared semantic resources (tools, data and importantly the communities) have enabled the application of semantic web technologies to facilitate semantic (context dependent) search across multiple data sources, which can be used by machine learning to produce better predictions by exploiting the semantic links in knowledge graphs and linked datasets.
Drug Discovery, Semantic Web, Ontologies, Semantic Search, Knowledge Graph, Linked Data, Inferencing
1746-0441
433-444
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f

Kanza, Samantha and Frey, Jeremy G. (2019) A new wave of innovation in semantic web tools for drug discovery. Expert Opinion on Drug Discovery, 14 (5), 433-444. (doi:10.1080/17460441.2019.1586880).

Record type: Review

Abstract

Introduction: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to dealing with the high levels of data for drug discovery. These technologies enable us to represent the data in a formal, structured, interoperable and comparable way, and to tease out undiscovered links between drug data (be it identifying new drug-targets or relevant compounds, or links between specific drugs and diseases).

Areas Covered: This review focuses on explaining how semantic web technologies are being used to aid advances in drug discovery. The main types of semantic web technologies are explained, outlining how they work and how they can be used in the drug discovery process, with a consideration of how the use of these technologies has progressed from their initial usage.

Expert Opinion: The increased availability of shared semantic resources (tools, data and importantly the communities) have enabled the application of semantic web technologies to facilitate semantic (context dependent) search across multiple data sources, which can be used by machine learning to produce better predictions by exploiting the semantic links in knowledge graphs and linked datasets.

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Accepted/In Press date: 21 February 2019
e-pub ahead of print date: 19 March 2019
Keywords: Drug Discovery, Semantic Web, Ontologies, Semantic Search, Knowledge Graph, Linked Data, Inferencing

Identifiers

Local EPrints ID: 429582
URI: https://eprints.soton.ac.uk/id/eprint/429582
ISSN: 1746-0441
PURE UUID: cea8de0f-1707-4ca5-9c9d-133d639b2aed
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 29 Mar 2019 17:30
Last modified: 20 Jul 2019 01:28

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