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» Combining microarrays and genetic analysis
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BMCBI
2004
158views more  BMCBI 2004»
13 years 9 months ago
Incremental genetic K-means algorithm and its application in gene expression data analysis
Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms suc...
Yi Lu, Shiyong Lu, Farshad Fotouhi, Youping Deng, ...
BMCBI
2007
171views more  BMCBI 2007»
13 years 9 months ago
Classification of microarray data using gene networks
Background: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) a...
Franck Rapaport, Andrei Zinovyev, Marie Dutreix, E...
BMCBI
2010
161views more  BMCBI 2010»
13 years 10 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
2010
96views more  BMCBI 2010»
13 years 10 months ago
A statistical framework for differential network analysis from microarray data
Background: It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of gene...
Ryan Gill, Somnath Datta, Susmita Datta
BMCBI
2004
180views more  BMCBI 2004»
13 years 9 months ago
Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovaria
Background: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identi...
Virginie M. Aris, Michael J. Cody, Jeff Cheng, Jam...