Optimising orbit counting of arbitrary order by equation selection
Background: Graphlets are useful for bioinformatics network analysis. Based on the structure of Hočevar and Demšar’s ORCA algorithm, we have created an orbit counting algorithm, named Jesse. This algorithm, like ORCA, uses equations to count the orbits, but unlike ORCA it can count graphlets of any...
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ftunivgent:oai:archive.ugent.be:8584069 2023-06-11T04:15:46+02:00 Optimising orbit counting of arbitrary order by equation selection Melckenbeeck, Ine Audenaert, P. Van Parys, Thomas Van de Peer, Yves Colle, Didier Pickavet, Mario 2019 application/pdf https://biblio.ugent.be/publication/8584069 http://hdl.handle.net/1854/LU-8584069 https://doi.org/10.1186/s12859-018-2483-9 https://biblio.ugent.be/publication/8584069/file/8584072 eng eng https://biblio.ugent.be/publication/8584069 http://hdl.handle.net/1854/LU-8584069 http://dx.doi.org/10.1186/s12859-018-2483-9 https://biblio.ugent.be/publication/8584069/file/8584072 Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) info:eu-repo/semantics/closedAccess BMC BIOINFORMATICS ISSN: 1471-2105 Science General graph theory graphlets orbits equations optimisation Cytoscape App SCALE-FREE journalArticle info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2019 ftunivgent https://doi.org/10.1186/s12859-018-2483-9 2023-05-10T22:51:49Z Background: Graphlets are useful for bioinformatics network analysis. Based on the structure of Hočevar and Demšar’s ORCA algorithm, we have created an orbit counting algorithm, named Jesse. This algorithm, like ORCA, uses equations to count the orbits, but unlike ORCA it can count graphlets of any order. To do so, it generates the required internal structures and equations automatically. Many more redundant equations are generated, however, and Jesse’s running time is highly dependent on which of these equations are used. Therefore, this paper aims to investigate which equations are most efficient, and which factors have an effect on this efficiency. Results: With appropriate equation selection, Jesse’s running time may be reduced by a factor of up to 2 in the best case, compared to using randomly selected equations. Which equations are most efficient depends on the density of the graph, but barely on the graph type. At low graph density, equations with terms in their right-hand side with few arguments are more efficient, whereas at high density, equations with terms with many arguments in the right-hand side are most efficient. At a density between 0.6 and 0.7, both types of equations are about equally efficient. Conclusions: Our Jesse algorithm became up to a factor 2 more efficient, by automatically selecting the best equations based on graph density. It was adapted into a Cytoscape App that is freely available from the Cytoscape App Store to ease application by bioinformaticians. Article in Journal/Newspaper Orca Ghent University Academic Bibliography BMC Bioinformatics 20 1 |
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Open Polar |
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Ghent University Academic Bibliography |
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ftunivgent |
language |
English |
topic |
Science General graph theory graphlets orbits equations optimisation Cytoscape App SCALE-FREE |
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Science General graph theory graphlets orbits equations optimisation Cytoscape App SCALE-FREE Melckenbeeck, Ine Audenaert, P. Van Parys, Thomas Van de Peer, Yves Colle, Didier Pickavet, Mario Optimising orbit counting of arbitrary order by equation selection |
topic_facet |
Science General graph theory graphlets orbits equations optimisation Cytoscape App SCALE-FREE |
description |
Background: Graphlets are useful for bioinformatics network analysis. Based on the structure of Hočevar and Demšar’s ORCA algorithm, we have created an orbit counting algorithm, named Jesse. This algorithm, like ORCA, uses equations to count the orbits, but unlike ORCA it can count graphlets of any order. To do so, it generates the required internal structures and equations automatically. Many more redundant equations are generated, however, and Jesse’s running time is highly dependent on which of these equations are used. Therefore, this paper aims to investigate which equations are most efficient, and which factors have an effect on this efficiency. Results: With appropriate equation selection, Jesse’s running time may be reduced by a factor of up to 2 in the best case, compared to using randomly selected equations. Which equations are most efficient depends on the density of the graph, but barely on the graph type. At low graph density, equations with terms in their right-hand side with few arguments are more efficient, whereas at high density, equations with terms with many arguments in the right-hand side are most efficient. At a density between 0.6 and 0.7, both types of equations are about equally efficient. Conclusions: Our Jesse algorithm became up to a factor 2 more efficient, by automatically selecting the best equations based on graph density. It was adapted into a Cytoscape App that is freely available from the Cytoscape App Store to ease application by bioinformaticians. |
format |
Article in Journal/Newspaper |
author |
Melckenbeeck, Ine Audenaert, P. Van Parys, Thomas Van de Peer, Yves Colle, Didier Pickavet, Mario |
author_facet |
Melckenbeeck, Ine Audenaert, P. Van Parys, Thomas Van de Peer, Yves Colle, Didier Pickavet, Mario |
author_sort |
Melckenbeeck, Ine |
title |
Optimising orbit counting of arbitrary order by equation selection |
title_short |
Optimising orbit counting of arbitrary order by equation selection |
title_full |
Optimising orbit counting of arbitrary order by equation selection |
title_fullStr |
Optimising orbit counting of arbitrary order by equation selection |
title_full_unstemmed |
Optimising orbit counting of arbitrary order by equation selection |
title_sort |
optimising orbit counting of arbitrary order by equation selection |
publishDate |
2019 |
url |
https://biblio.ugent.be/publication/8584069 http://hdl.handle.net/1854/LU-8584069 https://doi.org/10.1186/s12859-018-2483-9 https://biblio.ugent.be/publication/8584069/file/8584072 |
genre |
Orca |
genre_facet |
Orca |
op_source |
BMC BIOINFORMATICS ISSN: 1471-2105 |
op_relation |
https://biblio.ugent.be/publication/8584069 http://hdl.handle.net/1854/LU-8584069 http://dx.doi.org/10.1186/s12859-018-2483-9 https://biblio.ugent.be/publication/8584069/file/8584072 |
op_rights |
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.1186/s12859-018-2483-9 |
container_title |
BMC Bioinformatics |
container_volume |
20 |
container_issue |
1 |
_version_ |
1768372865329004544 |