Propagating semantic information in biochemical network models
<p>Abstract</p> <p>Background</p> <p>To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigni...
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doaj-618d5fafeb6c4205af963bbe6944b0672020-11-24T23:01:48ZengBMCBMC Bioinformatics1471-21052012-01-011311810.1186/1471-2105-13-18Propagating semantic information in biochemical network modelsSchulz MarvinKlipp EddaLiebermeister Wolfram<p>Abstract</p> <p>Background</p> <p>To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.</p> <p>Results</p> <p>A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.</p> <p>Conclusions</p> <p>Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at <url>http://www.semanticsbml.org</url>.</p> http://www.biomedcentral.com/1471-2105/13/18 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Schulz Marvin Klipp Edda Liebermeister Wolfram |
spellingShingle |
Schulz Marvin Klipp Edda Liebermeister Wolfram Propagating semantic information in biochemical network models BMC Bioinformatics |
author_facet |
Schulz Marvin Klipp Edda Liebermeister Wolfram |
author_sort |
Schulz Marvin |
title |
Propagating semantic information in biochemical network models |
title_short |
Propagating semantic information in biochemical network models |
title_full |
Propagating semantic information in biochemical network models |
title_fullStr |
Propagating semantic information in biochemical network models |
title_full_unstemmed |
Propagating semantic information in biochemical network models |
title_sort |
propagating semantic information in biochemical network models |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2012-01-01 |
description |
<p>Abstract</p> <p>Background</p> <p>To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation.</p> <p>Results</p> <p>A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements.</p> <p>Conclusions</p> <p>Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at <url>http://www.semanticsbml.org</url>.</p> |
url |
http://www.biomedcentral.com/1471-2105/13/18 |
work_keys_str_mv |
AT schulzmarvin propagatingsemanticinformationinbiochemicalnetworkmodels AT klippedda propagatingsemanticinformationinbiochemicalnetworkmodels AT liebermeisterwolfram propagatingsemanticinformationinbiochemicalnetworkmodels |
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