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» Gene set analysis for longitudinal gene expression data
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BMCBI
2004
158views more  BMCBI 2004»
13 years 7 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, ...
BIOINFORMATICS
2005
151views more  BIOINFORMATICS 2005»
13 years 7 months ago
Differential and trajectory methods for time course gene expression data
Motivation: The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differen...
Yulan Liang, Bamidele Tayo, Xueya Cai, Arpad Kelem...
CSB
2005
IEEE
146views Bioinformatics» more  CSB 2005»
14 years 1 months ago
Multi-Metric and Multi-Substructure Biclustering Analysis for Gene Expression Data
A good number of biclustering algorithms have been proposed for grouping gene expression data. Many of them have adopted matrix norms to define the similarity score of a bicluste...
Sun-Yuan Kung, Man-Wai Mak, Ilias Tagkopoulos
BMCBI
2008
126views more  BMCBI 2008»
13 years 7 months ago
Methods for evaluating gene expression from Affymetrix microarray datasets
Background: Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An import...
Ning Jiang, Lindsey J. Leach, Xiaohua Hu, Elena Po...
BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
13 years 9 months ago
Combined expression data with missing values and gene interaction network analysis: a Markovian integrated approach
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
Juliette Blanchet, Matthieu Vignes