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» Using Bayesian networks to analyze expression data
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BMCBI
2010
147views more  BMCBI 2010»
14 years 9 months ago
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Background: High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key w...
Thomas J. Hardcastle, Krystyna A. Kelly
BMCBI
2010
124views more  BMCBI 2010»
14 years 9 months ago
A factor model to analyze heterogeneity in gene expression
Background: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene d...
Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigu...
CLEIEJ
2007
152views more  CLEIEJ 2007»
14 years 9 months ago
Gene Expression Analysis using Markov Chains extracted from RNNs
Abstract. This paper present a new approach for the analysis of gene expression, by extracting a Markov Chain from trained Recurrent Neural Networks (RNNs). A lot of microarray dat...
Igor Lorenzato Almeida, Denise Regina Pechmann Sim...
BMCBI
2008
154views more  BMCBI 2008»
14 years 9 months ago
Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration
Background: This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascade...
Yulan Liang, Arpad Kelemen
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AUSAI
2006
Springer
15 years 1 months ago
Modular Bayesian Networks for Inferring Landmarks on Mobile Daily Life
Abstract. Mobile devices get to handle much information thanks to the convergence of diverse functionalities. Their environment has great potential of supporting customized service...
Keum-Sung Hwang, Sung-Bae Cho