Semi-supervised Approach for Finding Cancer Sub-classes on Gene Expression Data

被引：0

作者：

Ribeiro, Clerton ^{[1
]}

de Carvalho, Francisco de Assis T. ^{[1
]}

Costa, Ivan G. ^{[1
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil

来源：

ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY | 2010年 / 6268卷

关键词：

cancer gene expression; mixture discriminant analysis; semi-supervised learning; constraint based mixture estimation; CLASS DISCOVERY; CLASSIFICATION;

D O I：

暂无

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The analysis of cancer gene expression is intrinsically a semi-supervised problem, as one is interested in building a classifier for diagnosis, but also on finding new sub-classes of cancer. We propose here a method for Mixture Discriminant Analysis (MDA), which can simultaneously detect sub-classes of cancer and perform classification. We evaluate the method on 10 gene expression data sets. MDA not only improved the classification in some of these data sets, as it detected some known and putative sub-classes of cancer.

引用

页码：25 / 34

页数：10

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