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BIOINFORMATICS
2002
72views more  BIOINFORMATICS 2002»
13 years 8 months ago
Mixture modelling of gene expression data from microarray experiments
Debashis Ghosh, Arul M. Chinnaiyan
BMCBI
2007
127views more  BMCBI 2007»
13 years 8 months ago
A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments
Background: With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across...
Hyungwon Choi, Ronglai Shen, Arul M. Chinnaiyan, D...
FGCN
2008
IEEE
155views Communications» more  FGCN 2008»
13 years 10 months ago
Modeling the Marginal Distribution of Gene Expression with Mixture Models
We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal expr...
Edward Wijaya, Hajime Harada, Paul Horton
BMCBI
2004
158views more  BMCBI 2004»
13 years 8 months ago
A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data
Background: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two...
Kayvan Najarian, Maryam Zaheri, Ali Ajdari Rad, Si...
BMCBI
2008
107views more  BMCBI 2008»
13 years 8 months ago
A mixture model approach to sample size estimation in two-sample comparative microarray experiments
Background: Choosing the appropriate sample size is an important step in the design of a microarray experiment, and recently methods have been proposed that estimate sample sizes ...
Tommy S. Jørstad, Herman Midelfart, Atle M....