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» Gene set analysis for longitudinal gene expression data
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PSB
2003
13 years 9 months ago
Decomposing Gene Expression into Cellular Processes
We propose a probabilistic model for cellular processes, and an algorithm for discovering them from gene expression data. A process is associated with a set of genes that particip...
Eran Segal, Alexis Battle, Daphne Koller
BMCBI
2007
104views more  BMCBI 2007»
13 years 7 months ago
Joint mapping of genes and conditions via multidimensional unfolding analysis
Background: Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions base...
Katrijn Van Deun, Kathleen Marchal, Willem J. Heis...
BMCBI
2006
134views more  BMCBI 2006»
13 years 7 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
JCB
2000
103views more  JCB 2000»
13 years 7 months ago
Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data
Although two-color uorescent DNA microarrays are now standard equipment in many molecular biology laboratories, methods for identifying differentially expressed genes in microarra...
Trey Ideker, Vesteinn Thorsson, Andrew F. Siegel, ...
IJON
2008
128views more  IJON 2008»
13 years 7 months ago
Independent arrays or independent time courses for gene expression time series data analysis
In this paper we apply three different independent component analysis (ICA) methods, including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to gene exp...
Sookjeong Kim, Jong Kyoung Kim, Seungjin Choi