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 | |
| Publication status | Published - 2007 Jun 1 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
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Cancer classification using ensemble of neural networks with multiple significant gene subsets. / Cho, Sung Bae; Won, Hong Hee.
In: Applied Intelligence, Vol. 26, No. 3, 01.06.2007, p. 243-250.Research output: Contribution to journal › Article
TY - JOUR
T1 - Cancer classification using ensemble of neural networks with multiple significant gene subsets
AU - Cho, Sung Bae
AU - Won, Hong Hee
PY - 2007/6/1
Y1 - 2007/6/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=34247154511&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34247154511&partnerID=8YFLogxK
U2 - 10.1007/s10489-006-0020-4
DO - 10.1007/s10489-006-0020-4
M3 - Article
AN - SCOPUS:34247154511
VL - 26
SP - 243
EP - 250
JO - Applied Intelligence
JF - Applied Intelligence
SN - 0924-669X
IS - 3
ER -