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» Using Bayesian networks to analyze expression data
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BMCBI
2007
182views more  BMCBI 2007»
14 years 9 months ago
Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data
Background: ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct transcriptional regulatory networks. Howev...
Wei-Sheng Wu, Wen-Hsiung Li, Bor-Sen Chen
BMCBI
2010
101views more  BMCBI 2010»
14 years 10 months ago
Detection of copy number variation from array intensity and sequencing read depth using a stepwise Bayesian model
Background: Copy number variants (CNVs) have been demonstrated to occur at a high frequency and are now widely believed to make a significant contribution to the phenotypic variat...
Zhengdong D. Zhang, Mark B. Gerstein
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RECOMB
2010
Springer
15 years 4 months ago
Hierarchical Generative Biclustering for MicroRNA Expression Analysis
Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie ...
José Caldas, Samuel Kaski
ICASSP
2011
IEEE
14 years 1 months ago
Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery
This paper studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of li...
Yoann Altmann, Abderrahim Halimi, Nicolas Dobigeon...
BMCBI
2004
146views more  BMCBI 2004»
14 years 9 months ago
Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data
Background: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have...
Feng Gao, Barrett C. Foat, Harmen J. Bussemaker