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Knowledge-driven enhancements for task composition in bioinformatics

BACKGROUND: A key application area of semantic technologies is the fast-developing field of bioinformatics. Sealife was a project within this field with the aim of creating semantics-based web browsing capabilities for the Life Sciences. This includes meaningfully linking significant terms from the...

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Detalles Bibliográficos
Autores principales: Sutherland, Karen, McLeod, Kenneth, Ferguson, Gus, Burger, Albert
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755820/
https://www.ncbi.nlm.nih.gov/pubmed/19796396
http://dx.doi.org/10.1186/1471-2105-10-S10-S12
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author Sutherland, Karen
McLeod, Kenneth
Ferguson, Gus
Burger, Albert
author_facet Sutherland, Karen
McLeod, Kenneth
Ferguson, Gus
Burger, Albert
author_sort Sutherland, Karen
collection PubMed
description BACKGROUND: A key application area of semantic technologies is the fast-developing field of bioinformatics. Sealife was a project within this field with the aim of creating semantics-based web browsing capabilities for the Life Sciences. This includes meaningfully linking significant terms from the text of a web page to executable web services. It also involves the semantic mark-up of biological terms, linking them to biomedical ontologies, then discovering and executing services based on terms that interest the user. RESULTS: A system was produced which allows a user to identify terms of interest on a web page and subsequently connects these to a choice of web services which can make use of these inputs. Elements of Artificial Intelligence Planning build on this to present a choice of higher level goals, which can then be broken down to construct a workflow. An Argumentation System was implemented to evaluate the results produced by three different gene expression databases. An evaluation of these modules was carried out on users from a variety of backgrounds. Users with little knowledge of web services were able to achieve tasks that used several services in much less time than they would have taken to do this manually. The Argumentation System was also considered a useful resource and feedback was collected on the best way to present results. CONCLUSION: Overall the system represents a move forward in helping users to both construct workflows and analyse results by incorporating specific domain knowledge into the software. It also provides a mechanism by which web pages can be linked to web services. However, this work covers a specific domain and much co-ordinated effort is needed to make all web services available for use in such a way, i.e. the integration of underlying knowledge is a difficult but essential task.
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spelling pubmed-27558202009-10-03 Knowledge-driven enhancements for task composition in bioinformatics Sutherland, Karen McLeod, Kenneth Ferguson, Gus Burger, Albert BMC Bioinformatics Research BACKGROUND: A key application area of semantic technologies is the fast-developing field of bioinformatics. Sealife was a project within this field with the aim of creating semantics-based web browsing capabilities for the Life Sciences. This includes meaningfully linking significant terms from the text of a web page to executable web services. It also involves the semantic mark-up of biological terms, linking them to biomedical ontologies, then discovering and executing services based on terms that interest the user. RESULTS: A system was produced which allows a user to identify terms of interest on a web page and subsequently connects these to a choice of web services which can make use of these inputs. Elements of Artificial Intelligence Planning build on this to present a choice of higher level goals, which can then be broken down to construct a workflow. An Argumentation System was implemented to evaluate the results produced by three different gene expression databases. An evaluation of these modules was carried out on users from a variety of backgrounds. Users with little knowledge of web services were able to achieve tasks that used several services in much less time than they would have taken to do this manually. The Argumentation System was also considered a useful resource and feedback was collected on the best way to present results. CONCLUSION: Overall the system represents a move forward in helping users to both construct workflows and analyse results by incorporating specific domain knowledge into the software. It also provides a mechanism by which web pages can be linked to web services. However, this work covers a specific domain and much co-ordinated effort is needed to make all web services available for use in such a way, i.e. the integration of underlying knowledge is a difficult but essential task. BioMed Central 2009-10-01 /pmc/articles/PMC2755820/ /pubmed/19796396 http://dx.doi.org/10.1186/1471-2105-10-S10-S12 Text en © Sutherland et al; licensee BioMed Central Ltd. 2009 https://creativecommons.org/licenses/by/2.0/This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Sutherland, Karen
McLeod, Kenneth
Ferguson, Gus
Burger, Albert
Knowledge-driven enhancements for task composition in bioinformatics
title Knowledge-driven enhancements for task composition in bioinformatics
title_full Knowledge-driven enhancements for task composition in bioinformatics
title_fullStr Knowledge-driven enhancements for task composition in bioinformatics
title_full_unstemmed Knowledge-driven enhancements for task composition in bioinformatics
title_short Knowledge-driven enhancements for task composition in bioinformatics
title_sort knowledge-driven enhancements for task composition in bioinformatics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755820/
https://www.ncbi.nlm.nih.gov/pubmed/19796396
http://dx.doi.org/10.1186/1471-2105-10-S10-S12
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