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BMCBI
2007
146views more  BMCBI 2007»
15 years 6 months ago
Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data
Background: Transcriptional modules (TM) consist of groups of co-regulated genes and transcription factors (TF) regulating their expression. Two high-throughput (HT) experimental ...
Xiangdong Liu, Walter J. Jessen, Siva Sivaganesan,...
BMCBI
2006
103views more  BMCBI 2006»
15 years 6 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
CVPR
2009
IEEE
17 years 1 months ago
Learning Visual Flows: A Lie Algebraic Approach
We present a novel method for modeling dynamic visual phenomena, which consists of two key aspects. First, the in- tegral motion of constituent elements in a dynamic scene is ca...
Dahua Lin, W. Eric L. Grimson, John W. Fisher III
CVPR
2009
IEEE
17 years 1 months ago
Multiphase Geometric Couplings for the Segmentation of Neural Processes
The ability to constrain the geometry of deformable models for image segmentation can be useful when information about the expected shape or positioning of the objects in a scene i...
Amelio Vázquez Reina, Eric Miller, Hanspete...
CVPR
2000
IEEE
16 years 8 months ago
Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help?
Many problems in vision can be formulated as Bayesian inference. It is important to determine the accuracy of these inferences and how they depend on the problem domain. In recent...
Alan L. Yuille, James M. Coughlan, Song Chun Zhu, ...