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» Clustering cancer gene expression data: a comparative study
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BIBM
2009
IEEE
172views Bioinformatics» more  BIBM 2009»
14 years 1 months ago
Identifying Gene Signatures from Cancer Progression Data Using Ordinal Analysis
—A comprehensive understanding of cancer progression may shed light on genetic and molecular mechanisms of oncogenesis, and it may provide much needed information for effective d...
Yoon Soo Pyon, Jing Li
BMCBI
2002
195views more  BMCBI 2002»
13 years 8 months ago
Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study
Background: A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expressio...
Junbai Wang, Jan Delabie, Hans Christian Aasheim, ...
BMCBI
2005
112views more  BMCBI 2005»
13 years 8 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
2007
173views more  BMCBI 2007»
13 years 8 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
BMCBI
2010
155views more  BMCBI 2010»
13 years 8 months ago
A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer
Background: In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of...
Fan Shi, Christopher Leckie, Geoff MacIntyre, Izha...