Network module detection: Affinity search technique with the multi-node topological overlap measure

<p<Abstract</p< <p<Background</p< <p<Many clustering procedures only allow the user to input a <it<pairwise </it<dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, .....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Horvath Steve [verfasserIn] Li Ai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Übergeordnetes Werk:	In: BMC Research Notes - BMC, 2008, 2(2009), 1, p 142
Übergeordnetes Werk:	volume:2 ; year:2009 ; number:1, p 142

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1756-0500-2-142

Katalog-ID:	DOAJ063342499

Internformat


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allfieldsGer	10.1186/1756-0500-2-142 doi (DE-627)DOAJ063342499 (DE-599)DOAJ4c0b1032fefd40fea47a4bb4fb9c2002 DE-627 ger DE-627 rakwb eng QH301-705.5 Q1-390 Horvath Steve verfasserin aut Network module detection: Affinity search technique with the multi-node topological overlap measure 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Many clustering procedures only allow the user to input a <it<pairwise </it<dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high <it<multi-node </it<topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.</p< <p<Findings</p< <p<We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.</p< <p<Conclusion</p< <p<Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: <url<http://www.genetics.ucla.edu/labs/horvath/MTOM/</url<</p< Medicine R Biology (General) Science (General) Li Ai verfasserin aut In BMC Research Notes BMC, 2008 2(2009), 1, p 142 (DE-627)559431805 (DE-600)2413336-X 17560500 nnns volume:2 year:2009 number:1, p 142 https://doi.org/10.1186/1756-0500-2-142 kostenfrei https://doaj.org/article/4c0b1032fefd40fea47a4bb4fb9c2002 kostenfrei http://www.biomedcentral.com/1756-0500/2/142 kostenfrei https://doaj.org/toc/1756-0500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2009 1, p 142
allfieldsSound	10.1186/1756-0500-2-142 doi (DE-627)DOAJ063342499 (DE-599)DOAJ4c0b1032fefd40fea47a4bb4fb9c2002 DE-627 ger DE-627 rakwb eng QH301-705.5 Q1-390 Horvath Steve verfasserin aut Network module detection: Affinity search technique with the multi-node topological overlap measure 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Many clustering procedures only allow the user to input a <it<pairwise </it<dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high <it<multi-node </it<topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.</p< <p<Findings</p< <p<We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.</p< <p<Conclusion</p< <p<Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: <url<http://www.genetics.ucla.edu/labs/horvath/MTOM/</url<</p< Medicine R Biology (General) Science (General) Li Ai verfasserin aut In BMC Research Notes BMC, 2008 2(2009), 1, p 142 (DE-627)559431805 (DE-600)2413336-X 17560500 nnns volume:2 year:2009 number:1, p 142 https://doi.org/10.1186/1756-0500-2-142 kostenfrei https://doaj.org/article/4c0b1032fefd40fea47a4bb4fb9c2002 kostenfrei http://www.biomedcentral.com/1756-0500/2/142 kostenfrei https://doaj.org/toc/1756-0500 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2009 1, p 142
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author	Horvath Steve
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title	Network module detection: Affinity search technique with the multi-node topological overlap measure
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title_full	Network module detection: Affinity search technique with the multi-node topological overlap measure
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title_sort	network module detection: affinity search technique with the multi-node topological overlap measure
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title_auth	Network module detection: Affinity search technique with the multi-node topological overlap measure
abstract	<p<Abstract</p< <p<Background</p< <p<Many clustering procedures only allow the user to input a <it<pairwise </it<dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high <it<multi-node </it<topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.</p< <p<Findings</p< <p<We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.</p< <p<Conclusion</p< <p<Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: <url<http://www.genetics.ucla.edu/labs/horvath/MTOM/</url<</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<Many clustering procedures only allow the user to input a <it<pairwise </it<dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high <it<multi-node </it<topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.</p< <p<Findings</p< <p<We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.</p< <p<Conclusion</p< <p<Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: <url<http://www.genetics.ucla.edu/labs/horvath/MTOM/</url<</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<Many clustering procedures only allow the user to input a <it<pairwise </it<dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high <it<multi-node </it<topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis.</p< <p<Findings</p< <p<We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering.</p< <p<Conclusion</p< <p<Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: <url<http://www.genetics.ucla.edu/labs/horvath/MTOM/</url<</p<
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title_short	Network module detection: Affinity search technique with the multi-node topological overlap measure
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Network module detection: Affinity search technique with the multi-node topological overlap measure

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