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» Gene-set approach for expression pattern analysis
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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...
64
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CIKM
2004
Springer
15 years 3 months ago
Mining gene expression datasets using density-based clustering
Given the recent advancement of microarray technologies, we present a density-based clustering approach for the purpose of co-expressed gene cluster identification. The underlyin...
Seokkyung Chung, Jongeun Jun, Dennis McLeod
BMCBI
2007
135views more  BMCBI 2007»
14 years 9 months ago
Detecting multivariate differentially expressed genes
Background: Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariat...
Roland Nilsson, José M. Peña, Johan ...
BMCBI
2005
113views more  BMCBI 2005»
14 years 9 months ago
Pathway level analysis of gene expression using singular value decomposition
Background: A promising direction in the analysis of gene expression focuses on the changes in expression of specific predefined sets of genes that are known in advance to be rela...
John K. Tomfohr, Jun Lu, Thomas B. Kepler
BMCBI
2008
159views more  BMCBI 2008»
14 years 9 months ago
Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments
Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increas...
Hongya Zhao, Kwok-Leung Chan, Lee-Ming Cheng, Hong...