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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| Format: | Article |
| Language: | English |
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BMC
2004-10-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/5/172 |
| _version_ | 1828529902628896768 |
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| author | Deng Youping Fotouhi Farshad Lu Shiyong Lu Yi Brown Susan J |
| author_facet | Deng Youping Fotouhi Farshad Lu Shiyong Lu Yi Brown Susan J |
| author_sort | Deng Youping |
| collection | DOAJ |
| description | <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> |
| first_indexed | 2024-12-11T22:15:24Z |
| format | Article |
| id | doaj.art-68a2ceafb5e5424886310c09e05b2b57 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T22:15:24Z |
| publishDate | 2004-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-68a2ceafb5e5424886310c09e05b2b572022-12-22T00:48:37ZengBMCBMC Bioinformatics1471-21052004-10-015117210.1186/1471-2105-5-172Incremental genetic K-means algorithm and its application in gene expression data analysisDeng YoupingFotouhi FarshadLu ShiyongLu YiBrown Susan J<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>http://www.biomedcentral.com/1471-2105/5/172 |
| spellingShingle | Deng Youping Fotouhi Farshad Lu Shiyong Lu Yi Brown Susan J Incremental genetic K-means algorithm and its application in gene expression data analysis BMC Bioinformatics |
| title | Incremental genetic K-means algorithm and its application in gene expression data analysis |
| title_full | Incremental genetic K-means algorithm and its application in gene expression data analysis |
| title_fullStr | Incremental genetic K-means algorithm and its application in gene expression data analysis |
| title_full_unstemmed | Incremental genetic K-means algorithm and its application in gene expression data analysis |
| title_short | Incremental genetic K-means algorithm and its application in gene expression data analysis |
| title_sort | incremental genetic k means algorithm and its application in gene expression data analysis |
| url | http://www.biomedcentral.com/1471-2105/5/172 |
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