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» Clustering cancer gene expression data: a comparative study
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BMCBI
2006
123views more  BMCBI 2006»
15 years 6 months ago
Characterizing disease states from topological properties of transcriptional regulatory networks
Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. He...
David Tuck, Harriet Kluger, Yuval Kluger
BMCBI
2007
128views more  BMCBI 2007»
15 years 6 months ago
Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements
Background: Arabidopsis thaliana is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these ...
Hui Lan, Rachel Carson, Nicholas J. Provart, Antho...
BMCBI
2004
206views more  BMCBI 2004»
15 years 6 months ago
Combining gene expression data from different generations of oligonucleotide arrays
Background: One of the important challenges in microarray analysis is to take full advantage of previously accumulated data, both from one's own laboratory and from public re...
Kyu Baek Hwang, Sek Won Kong, Steven A. Greenberg,...
BMCBI
2008
114views more  BMCBI 2008»
15 years 6 months ago
Partial mixture model for tight clustering of gene expression time-course
Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively l...
Yinyin Yuan, Chang-Tsun Li, Roland Wilson
BMCBI
2007
143views more  BMCBI 2007»
15 years 6 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