oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Kavosh: a new algorithm for finding network motifs
Institute of Biochemistry and Biophysics, University of Tehran. (Laboratory of Systems Biology and Bioinformatics)
School of Mathematics and Computer Science, University of Tehran.
School of Biology, University of Tehran.
School of Mathematics and Computer Science, University of Tehran.
Show others and affiliations
2009 (English)In: BMC Bioinformatics, ISSN 1471-2105, Vol. 10, no 318Article in journal (Refereed) Published
Abstract [en]

Background

Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.

Results

We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network.

Conclusion

The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.

Place, publisher, year, edition, pages
BioMed Central: BioMed Central Ltd. , 2009. Vol. 10, no 318
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-24127DOI: 10.1186/1471-2105-10-318OAI: oai:DiVA.org:oru-24127DiVA: diva2:541191
Available from: 2012-08-07 Created: 2012-07-15 Last updated: 2012-08-07Bibliographically approved

Open Access in DiVA

Kashani-etal_Kavosh-BMCBioInf2009(1193 kB)105 downloads
File information
File name FULLTEXT01.pdfFile size 1193 kBChecksum SHA-512
92247f85f003af2c90685b880c8f31d59558766fabc9b4982a70f582caac181655cd387d3a3ab3d445bfd70d5f774c2e2f36f1ecdf5b62bc3e6536c9befed3a6
Type fulltextMimetype application/pdf

Other links

Publisher's full texthttp://www.biomedcentral.com/1471-2105/10/318

Search in DiVA

By author/editor
Asadi, Sahar
By organisation
School of Science and Technology, Örebro University, Sweden
In the same journal
BMC Bioinformatics
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 105 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 49 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf