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» Modeling, inference and optimization of regulatory networks ...
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BMCBI
2008
166views more  BMCBI 2008»
13 years 7 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
2010
172views more  BMCBI 2010»
13 years 2 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...
FLAIRS
2000
13 years 8 months ago
Inferencing Bayesian Networks from Time Series Data Using Natural Selection
This paper describes a new framework for using natural selection to evolve Bayesian Networks for use in forecasting time series data. It extends current research by introducing a ...
Andrew J. Novobilski, Farhad Kamangar
ICIC
2009
Springer
14 years 1 months ago
Solar Radiation Forecasting Using Ad-Hoc Time Series Preprocessing and Neural Networks
In this paper, we present an application of neural networks in the renewable energy domain. We have developed a methodology for the daily prediction of global solar radiation on a ...
Christophe Paoli, Cyril Voyant, Marc Muselli, Mari...
JMLR
2008
209views more  JMLR 2008»
13 years 7 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger