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» Memetic Algorithms for Feature Selection on Microarray Data
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BMCBI
2002
195views more  BMCBI 2002»
13 years 7 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
2007
134views more  BMCBI 2007»
13 years 7 months ago
Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both indiv...
Curtis Huttenhower, Avi I. Flamholz, Jessica N. La...
CAINE
2008
13 years 9 months ago
Hierarchical Clustering of Features on Categorical Data of Biomedical Applications
Data mining became increasingly important in bioinformatics and biomedical area during last decade. Various data mining methods, such as association rule mining and clustering, ha...
Yi Lu, Lily R. Liang
BMCBI
2006
120views more  BMCBI 2006»
13 years 7 months ago
An improved distance measure between the expression profiles linking co-expression and co-regulation in mouse
Background: Many statistical algorithms combine microarray expression data and genome sequence data to identify transcription factor binding motifs in the low eukaryotic genomes. ...
Ryung S. Kim, Hongkai Ji, Wing Hung Wong
BMCBI
2007
197views more  BMCBI 2007»
13 years 7 months ago
Boolean networks using the chi-square test for inferring large-scale gene regulatory networks
Background: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that...
Haseong Kim, Jae K. Lee, Taesung Park