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» Analysis of variance components in gene expression data
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WCE
2007
14 years 10 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
ICML
2004
IEEE
15 years 10 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
BMCBI
2005
112views more  BMCBI 2005»
14 years 9 months ago
Towards precise classification of cancers based on robust gene functional expression profiles
Background: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. Th...
Zheng Guo, Tianwen Zhang, Xia Li, Qi Wang, Jianzhe...
BMCBI
2005
153views more  BMCBI 2005»
14 years 9 months ago
Mining published lists of cancer related microarray experiments: Identification of a gene expression signature having a critical
Background: Routine application of gene expression microarray technology is rapidly producing large amounts of data that necessitate new approaches of analysis. The analysis of a ...
Giacomo Finocchiaro, Francesco Mancuso, Heiko M&uu...
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BMCBI
2006
134views more  BMCBI 2006»
14 years 9 months ago
An approach for clustering gene expression data with error information
Background: Clustering of gene expression patterns is a well-studied technique for elucidating trends across large numbers of transcripts and for identifying likely co-regulated g...
Brian Tjaden