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» Fractal Clustering for Microarray Data Analysis
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BMCBI
2002
195views more  BMCBI 2002»
14 years 9 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, ...
ICDM
2008
IEEE
146views Data Mining» more  ICDM 2008»
15 years 4 months ago
Hunting for Coherent Co-clusters in High Dimensional and Noisy Datasets
Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data analysis only a subset of the genes show cohesive exp...
Meghana Deodhar, Joydeep Ghosh, Gunjan Gupta, Hyuk...
BMCBI
2010
161views more  BMCBI 2010»
14 years 9 months ago
GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms
Background: An important objective of DNA microarray-based gene expression experimentation is determining interrelationships that exist between differentially expressed genes and ...
Saurin D. Jani, Gary L. Argraves, Jeremy L. Barth,...
BMCBI
2007
105views more  BMCBI 2007»
14 years 9 months ago
Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor sti
Background: High-throughput experiments, such as with DNA microarrays, typically result in hundreds of genes potentially relevant to the process under study, rendering the interpr...
Rob Jelier, Guido Jenster, Lambert C. J. Dorssers,...
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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