An evolutionary algorithm for discovering biclusters in gene expression data of breast cancer

Qinghua Huang; Minhua Lu; Hong Yan

doi:10.1109/CEC.2008.4630892

An evolutionary algorithm for discovering biclusters in gene expression data of breast cancer

Qinghua Huang, Minhua Lu, Hong Yan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.

Original language	English
Title of host publication	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Pages	829-834
Number of pages	6
DOIs	https://doi.org/10.1109/CEC.2008.4630892
State	Published - 2008
Externally published	Yes
Event	2008 IEEE Congress on Evolutionary Computation, CEC 2008 - Hong Kong, China Duration: 1 Jun 2008 → 6 Jun 2008

Publication series

Name	2008 IEEE Congress on Evolutionary Computation, CEC 2008

Conference

Conference	2008 IEEE Congress on Evolutionary Computation, CEC 2008
Country/Territory	China
City	Hong Kong
Period	1/06/08 → 6/06/08

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CEC.2008.4630892

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title = "An evolutionary algorithm for discovering biclusters in gene expression data of breast cancer",

abstract = "The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.",

author = "Qinghua Huang and Minhua Lu and Hong Yan",

year = "2008",

doi = "10.1109/CEC.2008.4630892",

language = "英语",

isbn = "9781424418237",

series = "2008 IEEE Congress on Evolutionary Computation, CEC 2008",

pages = "829--834",

booktitle = "2008 IEEE Congress on Evolutionary Computation, CEC 2008",

note = "2008 IEEE Congress on Evolutionary Computation, CEC 2008 ; Conference date: 01-06-2008 Through 06-06-2008",

}

Huang, Q, Lu, M & Yan, H 2008, An evolutionary algorithm for discovering biclusters in gene expression data of breast cancer. in 2008 IEEE Congress on Evolutionary Computation, CEC 2008., 4630892, 2008 IEEE Congress on Evolutionary Computation, CEC 2008, pp. 829-834, 2008 IEEE Congress on Evolutionary Computation, CEC 2008, Hong Kong, China, 1/06/08. https://doi.org/10.1109/CEC.2008.4630892

An evolutionary algorithm for discovering biclusters in gene expression data of breast cancer. / Huang, Qinghua; Lu, Minhua; Yan, Hong.
2008 IEEE Congress on Evolutionary Computation, CEC 2008. 2008. p. 829-834 4630892 (2008 IEEE Congress on Evolutionary Computation, CEC 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Huang, Qinghua

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N2 - The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.

AB - The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.

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T2 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008

Y2 - 1 June 2008 through 6 June 2008

ER -

An evolutionary algorithm for discovering biclusters in gene expression data of breast cancer

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