Simultaneous gene clustering and subset selection for sample classification via MDL
نویسندگان
چکیده
منابع مشابه
Simultaneous Gene Clustering and Subset Selection for Sample Classification Via MDL
MOTIVATION The microarray technology allows for the simultaneous monitoring of thousands of genes for each sample. The high-dimensional gene expression data can be used to study similarities of gene expression profiles across different samples to form a gene clustering. The clusters may be indicative of genetic pathways. Parallel to gene clustering is the important application of sample classif...
متن کاملSimultaneous Gene Clustering and Subset Selection for Sample Classi cation via MDL
Motivation: The microarray technology allows for the simultaneous monitoring of thousands of gene expression for each sample. The high-dimensional gene expression data can be used to study similarities of genes' expression pro les across di erent samples, that is gene clustering. The gene clusters may be indicative of genetic pathways. Another important application is sample classi cation, base...
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In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
متن کاملSimultaneous Clustering and Feature Selection Method for Gene Expression Data
Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression da...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2003
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btg039