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» Inferring Hidden Causal Structure
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JBI
2002
13 years 9 months ago
Revising regulatory networks: from expression data to linear causal models
Discovering the complex regulatory networks that govern mRNA expression is an important but difficult problem. Many current approaches use only expression data from microarrays to...
Stephen D. Bay, Jeff Shrager, Andrew Pohorille, Pa...
BMCBI
2008
118views more  BMCBI 2008»
13 years 10 months ago
Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling
Background: With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few appro...
Grace S. Shieh, Chung-Ming Chen, Ching-Yun Yu, Jui...
ISBI
2007
IEEE
14 years 4 months ago
Shape Analysis Using Curvature-Based Descriptors and Profile Hidden Markov Models
This paper presents a new framework for shape modeling and analysis. A shape instance is described by a curvature-based shape descriptor. A Profile Hidden Markov Model (PHMM) is ...
Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
AIEDU
2005
185views more  AIEDU 2005»
13 years 9 months ago
A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory
The Bayesian framework offers a number of techniques for inferring an individual's knowledge state from evidence of mastery of concepts or skills. A typical application where ...
Michel C. Desmarais, Xiaoming Pu
SSPR
2004
Springer
14 years 3 months ago
Tracking the Evolution of a Tennis Match Using Hidden Markov Models
The creation of a cognitive perception systems capable of inferring higher-level semantic information from low-level feature and event information for a given type of multimedia co...
Ilias Kolonias, William J. Christmas, Josef Kittle...