Incremental genetic K-means algorithm and its application in gene expression data analysis

<p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partiti...
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Autor*in:	Deng Youping [verfasserIn] Fotouhi Farshad [verfasserIn] Lu Shiyong [verfasserIn] Lu Yi [verfasserIn] Brown Susan J [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2004

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 5(2004), 1, p 172
Übergeordnetes Werk:	volume:5 ; year:2004 ; number:1, p 172

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1471-2105-5-172

Katalog-ID:	DOAJ040597989

Internformat


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520			\|a <p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p<
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allfields	10.1186/1471-2105-5-172 doi (DE-627)DOAJ040597989 (DE-599)DOAJ68a2ceafb5e5424886310c09e05b2b57 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Deng Youping verfasserin aut Incremental genetic K-means algorithm and its application in gene expression data analysis 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p< Computer applications to medicine. Medical informatics Biology (General) Fotouhi Farshad verfasserin aut Lu Shiyong verfasserin aut Lu Yi verfasserin aut Brown Susan J verfasserin aut In BMC Bioinformatics BMC, 2003 5(2004), 1, p 172 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:5 year:2004 number:1, p 172 https://doi.org/10.1186/1471-2105-5-172 kostenfrei https://doaj.org/article/68a2ceafb5e5424886310c09e05b2b57 kostenfrei http://www.biomedcentral.com/1471-2105/5/172 kostenfrei https://doaj.org/toc/1471-2105 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2004 1, p 172
spelling	10.1186/1471-2105-5-172 doi (DE-627)DOAJ040597989 (DE-599)DOAJ68a2ceafb5e5424886310c09e05b2b57 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Deng Youping verfasserin aut Incremental genetic K-means algorithm and its application in gene expression data analysis 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p< Computer applications to medicine. Medical informatics Biology (General) Fotouhi Farshad verfasserin aut Lu Shiyong verfasserin aut Lu Yi verfasserin aut Brown Susan J verfasserin aut In BMC Bioinformatics BMC, 2003 5(2004), 1, p 172 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:5 year:2004 number:1, p 172 https://doi.org/10.1186/1471-2105-5-172 kostenfrei https://doaj.org/article/68a2ceafb5e5424886310c09e05b2b57 kostenfrei http://www.biomedcentral.com/1471-2105/5/172 kostenfrei https://doaj.org/toc/1471-2105 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2004 1, p 172
allfields_unstemmed	10.1186/1471-2105-5-172 doi (DE-627)DOAJ040597989 (DE-599)DOAJ68a2ceafb5e5424886310c09e05b2b57 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Deng Youping verfasserin aut Incremental genetic K-means algorithm and its application in gene expression data analysis 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p< Computer applications to medicine. Medical informatics Biology (General) Fotouhi Farshad verfasserin aut Lu Shiyong verfasserin aut Lu Yi verfasserin aut Brown Susan J verfasserin aut In BMC Bioinformatics BMC, 2003 5(2004), 1, p 172 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:5 year:2004 number:1, p 172 https://doi.org/10.1186/1471-2105-5-172 kostenfrei https://doaj.org/article/68a2ceafb5e5424886310c09e05b2b57 kostenfrei http://www.biomedcentral.com/1471-2105/5/172 kostenfrei https://doaj.org/toc/1471-2105 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2004 1, p 172
allfieldsGer	10.1186/1471-2105-5-172 doi (DE-627)DOAJ040597989 (DE-599)DOAJ68a2ceafb5e5424886310c09e05b2b57 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Deng Youping verfasserin aut Incremental genetic K-means algorithm and its application in gene expression data analysis 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p< Computer applications to medicine. Medical informatics Biology (General) Fotouhi Farshad verfasserin aut Lu Shiyong verfasserin aut Lu Yi verfasserin aut Brown Susan J verfasserin aut In BMC Bioinformatics BMC, 2003 5(2004), 1, p 172 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:5 year:2004 number:1, p 172 https://doi.org/10.1186/1471-2105-5-172 kostenfrei https://doaj.org/article/68a2ceafb5e5424886310c09e05b2b57 kostenfrei http://www.biomedcentral.com/1471-2105/5/172 kostenfrei https://doaj.org/toc/1471-2105 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2004 1, p 172
allfieldsSound	10.1186/1471-2105-5-172 doi (DE-627)DOAJ040597989 (DE-599)DOAJ68a2ceafb5e5424886310c09e05b2b57 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Deng Youping verfasserin aut Incremental genetic K-means algorithm and its application in gene expression data analysis 2004 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p< Computer applications to medicine. Medical informatics Biology (General) Fotouhi Farshad verfasserin aut Lu Shiyong verfasserin aut Lu Yi verfasserin aut Brown Susan J verfasserin aut In BMC Bioinformatics BMC, 2003 5(2004), 1, p 172 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:5 year:2004 number:1, p 172 https://doi.org/10.1186/1471-2105-5-172 kostenfrei https://doaj.org/article/68a2ceafb5e5424886310c09e05b2b57 kostenfrei http://www.biomedcentral.com/1471-2105/5/172 kostenfrei https://doaj.org/toc/1471-2105 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2004 1, p 172
language	English
source	In BMC Bioinformatics 5(2004), 1, p 172 volume:5 year:2004 number:1, p 172
sourceStr	In BMC Bioinformatics 5(2004), 1, p 172 volume:5 year:2004 number:1, p 172
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Biology (General)
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container_title	BMC Bioinformatics
authorswithroles_txt_mv	Deng Youping @@aut@@ Fotouhi Farshad @@aut@@ Lu Shiyong @@aut@@ Lu Yi @@aut@@ Brown Susan J @@aut@@
publishDateDaySort_date	2004-01-01T00:00:00Z
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callnumber-first	R - Medicine
author	Deng Youping
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topic_title	R858-859.7 QH301-705.5 Incremental genetic K-means algorithm and its application in gene expression data analysis
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title	Incremental genetic K-means algorithm and its application in gene expression data analysis
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title_full	Incremental genetic K-means algorithm and its application in gene expression data analysis
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title_sort	incremental genetic k-means algorithm and its application in gene expression data analysis
callnumber	R858-859.7
title_auth	Incremental genetic K-means algorithm and its application in gene expression data analysis
abstract	<p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.</p< <p<Results</p< <p<In this paper, we propose a new clustering algorithm, <it<Incremental Genetic K-means Algorithm (IGKA)</it<. IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (<it<FGKA</it<). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at <url<http://database.cs.wayne.edu/proj/FGKA/index.htm.</url<</p< <p<Conclusions</p< <p<Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.</p<
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Incremental genetic K-means algorithm and its application in gene expression data analysis

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