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» Gene networks inference using dynamic Bayesian networks
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CEC
2003
IEEE
15 years 10 months ago
Stochastic neural network models for gene regulatory networks
AbstractRecent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of geno...
Tianhai Tian, Kevin Burrage
BIOINFORMATICS
2005
152views more  BIOINFORMATICS 2005»
15 years 4 months ago
Intervention in context-sensitive probabilistic Boolean networks
Motivation: Intervention in a gene regulatory network is used to help it avoid undesirable states, such as those associated with a disease. Several types of intervention have been...
Ranadip Pal, Aniruddha Datta, Michael L. Bittner, ...
BMCBI
2010
172views more  BMCBI 2010»
14 years 11 months ago
Nonparametric identification of regulatory interactions from spatial and temporal gene expression data
Background: The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but ...
Anil Aswani, Soile V. E. Keränen, James Brown...
BMCBI
2010
97views more  BMCBI 2010»
14 years 11 months ago
A semi-parametric Bayesian model for unsupervised differential co-expression analysis
Background: Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined ...
Johannes M. Freudenberg, Siva Sivaganesan, Michael...
161
Voted
PRL
2000
182views more  PRL 2000»
15 years 4 months ago
Bayesian MLP neural networks for image analysis
We demonstrate the advantages of using Bayesian multi layer perceptron (MLP) neural networks for image analysis. The Bayesian approach provides consistent way to do inference by c...
Aki Vehtari, Jouko Lampinen