Improving accuracy for cancer classification with a new algorithm for genes selection

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成果类型：

期刊论文

作者：

Zhang, Hongyan;Wang, Haiyan*;Dai, Zhijun;Chen, Ming-shun;Yuan, Zheming

通讯作者：

Wang, Haiyan

作者机构：

[Zhang, Hongyan; Dai, Zhijun; Yuan, Zheming] Hunan Prov Key Lab Crop Germplasm Innovat & Utili, Changsha 410128, Hunan, Peoples R China.

[Zhang, Hongyan; Dai, Zhijun; Yuan, Zheming] Hunan Agr Univ, Coll Biosafety Sci & Technol, Changsha 410128, Hunan, Peoples R China.

[Wang, Haiyan] Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA.

[Chen, Ming-shun] Kansas State Univ, USDA ARS, Manhattan, KS 66506 USA.

[Chen, Ming-shun] Kansas State Univ, Dept Entomol, Manhattan, KS 66506 USA.

通讯机构：

[Wang, Haiyan] K

Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA.

语种：

英文

关键词：

Linear Discriminant Analysis;Support Vector Machine Classifier;Feature Subset;Quadratic Discriminant Analysis;Informative Gene

期刊：

BMC Bioinformatics

ISSN：

1471-2105

年：

2012

卷：

期：

页码：

298

DOI：

10.1186/1471-2105-13-298

基金类别：

Science Foundation for Distinguished Young Scholars of Hunan Province, ChinaNational Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars [10JJ1005]; Research Fund for the Doctoral Program of Higher Education of ChinaResearch Fund for the Doctoral Program of Higher Education of China (RFDP)Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) [200805370002]; Hunan Provincial Education DepartmentHunan Provincial Education Department [11C0654]; Simons Foundation [246077]

机构署名：

本校为其他机构

院系归属：

植物保护学院

信息科学技术学院

摘要：

Background: Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BM...

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Improving accuracy for cancer classification with a new algorithm for genes selection

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