RDFScape: Semantic Web meets Systems Biology

<p>Abstract</p> <p>Background</p> <p>The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of...

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Main Author: Splendiani Andrea
Format: Article
Language:English
Published: BMC 2008-04-01
Series:BMC Bioinformatics
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spelling doaj-ef88ae9b9fa243d5b16d2fec753f191a2020-11-25T00:37:58ZengBMCBMC Bioinformatics1471-21052008-04-019Suppl 4S610.1186/1471-2105-9-S4-S6RDFScape: Semantic Web meets Systems BiologySplendiani Andrea<p>Abstract</p> <p>Background</p> <p>The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base.</p> <p>Results</p> <p>RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented.</p> <p>Conclusions</p> <p>The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.</p>
collection DOAJ
language English
format Article
sources DOAJ
author Splendiani Andrea
spellingShingle Splendiani Andrea
RDFScape: Semantic Web meets Systems Biology
BMC Bioinformatics
author_facet Splendiani Andrea
author_sort Splendiani Andrea
title RDFScape: Semantic Web meets Systems Biology
title_short RDFScape: Semantic Web meets Systems Biology
title_full RDFScape: Semantic Web meets Systems Biology
title_fullStr RDFScape: Semantic Web meets Systems Biology
title_full_unstemmed RDFScape: Semantic Web meets Systems Biology
title_sort rdfscape: semantic web meets systems biology
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2008-04-01
description <p>Abstract</p> <p>Background</p> <p>The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base.</p> <p>Results</p> <p>RDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented.</p> <p>Conclusions</p> <p>The use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.</p>
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