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FGCN
2008
IEEE
155views Communications» more  FGCN 2008»
13 years 11 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
2010
140views more  BMCBI 2010»
13 years 10 months ago
Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection
Background: Liquid chromatography-mass spectrometry (LC-MS) is one of the major techniques for the quantification of metabolites in complex biological samples. Peak modeling is on...
Tianwei Yu, Hesen Peng
DAGM
2010
Springer
13 years 11 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
CSL
1998
Springer
13 years 9 months ago
Model parameter estimation for mixture density polynomial segment models
In this paper, we propose parameter estimation techniques for mixture density polynomial segment models (MDPSMs) where their trajectories are specified with an arbitrary regressi...
Toshiaki Fukada, Kuldip K. Paliwal, Yoshinori Sagi...
BMCBI
2006
103views more  BMCBI 2006»
13 years 10 months ago
Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
Background: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level ...
Sébastien Lemieux