Cancer classification using ensemble of neural networks with multiple significant gene subsets

Sung Bae Cho; Hong Hee Won

doi:10.1007/s10489-006-0020-4

Cancer classification using ensemble of neural networks with multiple significant gene subsets

Sung Bae Cho, Hong Hee Won

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

84 Citations (Scopus)

Abstract

Molecular level diagnostics based on microarray technologies can offer the methodology of precise, objective, and systematic cancer classification. Genome-wide expression patterns generally consist of thousands of genes. It is desirable to extract some significant genes for accurate diagnosis of cancer because not all genes are associated with a cancer. In this paper, we have used representative gene vectors that are highly discriminatory for cancer classes and extracted multiple significant gene subsets based on those representative vectors respectively. Also, an ensemble of neural networks learned from the multiple significant gene subsets is proposed to classify a sample into one of several cancer classes. The performance of the proposed method is systematically evaluated using three different cancer types: Leukemia, colon, and B-cell lymphoma.

Original language	English
Pages (from-to)	243-250
Number of pages	8
Journal	Applied Intelligence
Volume	26
Issue number	3
DOIs	https://doi.org/10.1007/s10489-006-0020-4
Publication status	Published - 2007 Jun

Bibliographical note

Funding Information:
Acknowledgments This research was supported by the Ministry of Information and Communication, Korea under the Information Technology Research Center support program supervised by the Institute of Information Technology Assessment, IITA-2005-(C1090-0501-0019). The authors would like to thank the four anonymous reviewers for their constructive comments on the preliminary draft of this paper.

All Science Journal Classification (ASJC) codes

Artificial Intelligence

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10.1007/s10489-006-0020-4

Cite this

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abstract = "Molecular level diagnostics based on microarray technologies can offer the methodology of precise, objective, and systematic cancer classification. Genome-wide expression patterns generally consist of thousands of genes. It is desirable to extract some significant genes for accurate diagnosis of cancer because not all genes are associated with a cancer. In this paper, we have used representative gene vectors that are highly discriminatory for cancer classes and extracted multiple significant gene subsets based on those representative vectors respectively. Also, an ensemble of neural networks learned from the multiple significant gene subsets is proposed to classify a sample into one of several cancer classes. The performance of the proposed method is systematically evaluated using three different cancer types: Leukemia, colon, and B-cell lymphoma.",

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AU - Won, Hong Hee

N1 - Funding Information: Acknowledgments This research was supported by the Ministry of Information and Communication, Korea under the Information Technology Research Center support program supervised by the Institute of Information Technology Assessment, IITA-2005-(C1090-0501-0019). The authors would like to thank the four anonymous reviewers for their constructive comments on the preliminary draft of this paper.

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AB - Molecular level diagnostics based on microarray technologies can offer the methodology of precise, objective, and systematic cancer classification. Genome-wide expression patterns generally consist of thousands of genes. It is desirable to extract some significant genes for accurate diagnosis of cancer because not all genes are associated with a cancer. In this paper, we have used representative gene vectors that are highly discriminatory for cancer classes and extracted multiple significant gene subsets based on those representative vectors respectively. Also, an ensemble of neural networks learned from the multiple significant gene subsets is proposed to classify a sample into one of several cancer classes. The performance of the proposed method is systematically evaluated using three different cancer types: Leukemia, colon, and B-cell lymphoma.

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Cancer classification using ensemble of neural networks with multiple significant gene subsets

Abstract

Bibliographical note

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