Determining modular organization of protein interaction networks by maximizing modularity density

Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of protein...
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Autor*in:	Zhang, Shihua [verfasserIn] Ning, Xue-Mei Ding, Chris Zhang, Xiang-Sun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Functional Module Biological Network Simulated Network Protein Interaction Network Modular Organization

Anmerkung:	© Zhang et al; licensee BioMed Central Ltd. 2010

Übergeordnetes Werk:	Enthalten in: BMC systems biology - London : BioMed Central, 2007, 4(2010), Suppl 2 vom: 13. Sept.
Übergeordnetes Werk:	volume:4 ; year:2010 ; number:Suppl 2 ; day:13 ; month:09

Links:	Volltext

DOI / URN:	10.1186/1752-0509-4-S2-S10

Katalog-ID:	SPR028409590

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520			\|a Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method.
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allfields	10.1186/1752-0509-4-S2-S10 doi (DE-627)SPR028409590 (SPR)1752-0509-4-S2-S10-e DE-627 ger DE-627 rakwb eng Zhang, Shihua verfasserin aut Determining modular organization of protein interaction networks by maximizing modularity density 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhang et al; licensee BioMed Central Ltd. 2010 Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. Functional Module (dpeaa)DE-He213 Biological Network (dpeaa)DE-He213 Simulated Network (dpeaa)DE-He213 Protein Interaction Network (dpeaa)DE-He213 Modular Organization (dpeaa)DE-He213 Ning, Xue-Mei aut Ding, Chris aut Zhang, Xiang-Sun aut Enthalten in BMC systems biology London : BioMed Central, 2007 4(2010), Suppl 2 vom: 13. Sept. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:4 year:2010 number:Suppl 2 day:13 month:09 https://dx.doi.org/10.1186/1752-0509-4-S2-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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 4 2010 Suppl 2 13 09
spelling	10.1186/1752-0509-4-S2-S10 doi (DE-627)SPR028409590 (SPR)1752-0509-4-S2-S10-e DE-627 ger DE-627 rakwb eng Zhang, Shihua verfasserin aut Determining modular organization of protein interaction networks by maximizing modularity density 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhang et al; licensee BioMed Central Ltd. 2010 Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. Functional Module (dpeaa)DE-He213 Biological Network (dpeaa)DE-He213 Simulated Network (dpeaa)DE-He213 Protein Interaction Network (dpeaa)DE-He213 Modular Organization (dpeaa)DE-He213 Ning, Xue-Mei aut Ding, Chris aut Zhang, Xiang-Sun aut Enthalten in BMC systems biology London : BioMed Central, 2007 4(2010), Suppl 2 vom: 13. Sept. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:4 year:2010 number:Suppl 2 day:13 month:09 https://dx.doi.org/10.1186/1752-0509-4-S2-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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 4 2010 Suppl 2 13 09
allfields_unstemmed	10.1186/1752-0509-4-S2-S10 doi (DE-627)SPR028409590 (SPR)1752-0509-4-S2-S10-e DE-627 ger DE-627 rakwb eng Zhang, Shihua verfasserin aut Determining modular organization of protein interaction networks by maximizing modularity density 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhang et al; licensee BioMed Central Ltd. 2010 Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. Functional Module (dpeaa)DE-He213 Biological Network (dpeaa)DE-He213 Simulated Network (dpeaa)DE-He213 Protein Interaction Network (dpeaa)DE-He213 Modular Organization (dpeaa)DE-He213 Ning, Xue-Mei aut Ding, Chris aut Zhang, Xiang-Sun aut Enthalten in BMC systems biology London : BioMed Central, 2007 4(2010), Suppl 2 vom: 13. Sept. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:4 year:2010 number:Suppl 2 day:13 month:09 https://dx.doi.org/10.1186/1752-0509-4-S2-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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 4 2010 Suppl 2 13 09
allfieldsGer	10.1186/1752-0509-4-S2-S10 doi (DE-627)SPR028409590 (SPR)1752-0509-4-S2-S10-e DE-627 ger DE-627 rakwb eng Zhang, Shihua verfasserin aut Determining modular organization of protein interaction networks by maximizing modularity density 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhang et al; licensee BioMed Central Ltd. 2010 Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. Functional Module (dpeaa)DE-He213 Biological Network (dpeaa)DE-He213 Simulated Network (dpeaa)DE-He213 Protein Interaction Network (dpeaa)DE-He213 Modular Organization (dpeaa)DE-He213 Ning, Xue-Mei aut Ding, Chris aut Zhang, Xiang-Sun aut Enthalten in BMC systems biology London : BioMed Central, 2007 4(2010), Suppl 2 vom: 13. Sept. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:4 year:2010 number:Suppl 2 day:13 month:09 https://dx.doi.org/10.1186/1752-0509-4-S2-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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 4 2010 Suppl 2 13 09
allfieldsSound	10.1186/1752-0509-4-S2-S10 doi (DE-627)SPR028409590 (SPR)1752-0509-4-S2-S10-e DE-627 ger DE-627 rakwb eng Zhang, Shihua verfasserin aut Determining modular organization of protein interaction networks by maximizing modularity density 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhang et al; licensee BioMed Central Ltd. 2010 Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. Functional Module (dpeaa)DE-He213 Biological Network (dpeaa)DE-He213 Simulated Network (dpeaa)DE-He213 Protein Interaction Network (dpeaa)DE-He213 Modular Organization (dpeaa)DE-He213 Ning, Xue-Mei aut Ding, Chris aut Zhang, Xiang-Sun aut Enthalten in BMC systems biology London : BioMed Central, 2007 4(2010), Suppl 2 vom: 13. Sept. (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:4 year:2010 number:Suppl 2 day:13 month:09 https://dx.doi.org/10.1186/1752-0509-4-S2-S10 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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 4 2010 Suppl 2 13 09
language	English
source	Enthalten in BMC systems biology 4(2010), Suppl 2 vom: 13. Sept. volume:4 year:2010 number:Suppl 2 day:13 month:09
sourceStr	Enthalten in BMC systems biology 4(2010), Suppl 2 vom: 13. Sept. volume:4 year:2010 number:Suppl 2 day:13 month:09
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authorswithroles_txt_mv	Zhang, Shihua @@aut@@ Ning, Xue-Mei @@aut@@ Ding, Chris @@aut@@ Zhang, Xiang-Sun @@aut@@
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Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. 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author	Zhang, Shihua
spellingShingle	Zhang, Shihua misc Functional Module misc Biological Network misc Simulated Network misc Protein Interaction Network misc Modular Organization Determining modular organization of protein interaction networks by maximizing modularity density
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topic_title	Determining modular organization of protein interaction networks by maximizing modularity density Functional Module (dpeaa)DE-He213 Biological Network (dpeaa)DE-He213 Simulated Network (dpeaa)DE-He213 Protein Interaction Network (dpeaa)DE-He213 Modular Organization (dpeaa)DE-He213
topic	misc Functional Module misc Biological Network misc Simulated Network misc Protein Interaction Network misc Modular Organization
topic_unstemmed	misc Functional Module misc Biological Network misc Simulated Network misc Protein Interaction Network misc Modular Organization
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title	Determining modular organization of protein interaction networks by maximizing modularity density
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title_full	Determining modular organization of protein interaction networks by maximizing modularity density
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title_sort	determining modular organization of protein interaction networks by maximizing modularity density
title_auth	Determining modular organization of protein interaction networks by maximizing modularity density
abstract	Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. © Zhang et al; licensee BioMed Central Ltd. 2010
abstractGer	Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. © Zhang et al; licensee BioMed Central Ltd. 2010
abstract_unstemmed	Background With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable. Results The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network. Conclusions Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method. © Zhang et al; licensee BioMed Central Ltd. 2010
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title_short	Determining modular organization of protein interaction networks by maximizing modularity density
url	https://dx.doi.org/10.1186/1752-0509-4-S2-S10
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author2	Ning, Xue-Mei Ding, Chris Zhang, Xiang-Sun
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up_date	2024-07-03T19:18:51.587Z
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Determining modular organization of protein interaction networks by maximizing modularity density

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