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» Clustering gene expression patterns
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BMCBI
2007
143views more  BMCBI 2007»
14 years 10 months ago
Gene selection for classification of microarray data based on the Bayes error
Background: With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, ...
Ji-Gang Zhang, Hong-Wen Deng
RECOMB
2003
Springer
15 years 10 months ago
Whole-genome comparative annotation and regulatory motif discovery in multiple yeast species
In [13] we reported the genome sequences of S. paradoxus, S. mikatae and S. bayanus and compared these three yeast species to their close relative, S. cerevisiae. Genome-wide comp...
Manolis Kamvysselis, Nick Patterson, Bruce Birren,...
APBC
2004
138views Bioinformatics» more  APBC 2004»
14 years 11 months ago
Whole-Genome Functional Classification of Genes by Latent Semantic Analysis on Microarray Data
Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. The resulti...
See-Kiong Ng, Zexuan Zhu, Yew-Soon Ong
ICMLA
2010
14 years 7 months ago
Smoothing Gene Expression Using Biological Networks
Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify inform...
Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
DMKD
2003
ACM
96views Data Mining» more  DMKD 2003»
15 years 3 months ago
Using transposition for pattern discovery from microarray data
We analyze expression matrices to identify a priori interesting sets of genes, e.g., genes that are frequently co-regulated. Such matrices provide expression values for given biol...
François Rioult, Jean-François Bouli...