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Biological network motif detection and evaluation
BACKGROUND: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves co...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287573/ https://www.ncbi.nlm.nih.gov/pubmed/22784624 http://dx.doi.org/10.1186/1752-0509-5-S3-S5 |
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author | Kim, Wooyoung Li, Min Wang, Jianxin Pan, Yi |
author_facet | Kim, Wooyoung Li, Min Wang, Jianxin Pan, Yi |
author_sort | Kim, Wooyoung |
collection | PubMed |
description | BACKGROUND: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. RESULTS: We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. CONCLUSION: We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks. |
format | Online Article Text |
id | pubmed-3287573 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32875732012-03-01 Biological network motif detection and evaluation Kim, Wooyoung Li, Min Wang, Jianxin Pan, Yi BMC Syst Biol Research Article BACKGROUND: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. RESULTS: We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. CONCLUSION: We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks. BioMed Central 2011-12-23 /pmc/articles/PMC3287573/ /pubmed/22784624 http://dx.doi.org/10.1186/1752-0509-5-S3-S5 Text en Copyright ©2011 Kim et al. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 Article Kim, Wooyoung Li, Min Wang, Jianxin Pan, Yi Biological network motif detection and evaluation |
title | Biological network motif detection and evaluation |
title_full | Biological network motif detection and evaluation |
title_fullStr | Biological network motif detection and evaluation |
title_full_unstemmed | Biological network motif detection and evaluation |
title_short | Biological network motif detection and evaluation |
title_sort | biological network motif detection and evaluation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287573/ https://www.ncbi.nlm.nih.gov/pubmed/22784624 http://dx.doi.org/10.1186/1752-0509-5-S3-S5 |
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