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» Gene networks inference using dynamic Bayesian networks
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ACSC
2009
IEEE
15 years 6 months ago
Inference of Gene Expression Networks Using Memetic Gene Expression Programming
In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression n...
Armita Zarnegar, Peter Vamplew, Andrew Stranieri
BMCBI
2008
166views more  BMCBI 2008»
14 years 11 months ago
Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual informa
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
Weijun Luo, Kurt D. Hankenson, Peter J. Woolf
BMCBI
2005
88views more  BMCBI 2005»
14 years 11 months ago
Using large-scale perturbations in gene network reconstruction
Background: Recent analysis of the yeast gene network shows that most genes have few inputs, indicating that enumerative gene reconstruction methods are both useful and computatio...
Thomas MacCarthy, Andrew Pomiankowski, Robert Seym...
BMCBI
2008
118views more  BMCBI 2008»
14 years 11 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...
NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 3 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani