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IDA
2005
Springer
14 years 1 months ago
Bayesian Networks Learning for Gene Expression Datasets
DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network ...
Giacomo Gamberoni, Evelina Lamma, Fabrizio Riguzzi...
JMLR
2010
125views more  JMLR 2010»
13 years 2 months ago
On utility of gene set signatures in gene expression-based cancer class prediction
Machine learning methods that can use additional knowledge in their inference process are central to the development of integrative bioinformatics. Inclusion of background knowled...
Minca Mramor, Marko Toplak, Gregor Leban, Tomaz Cu...
BMCBI
2007
174views more  BMCBI 2007»
13 years 7 months ago
Inferring activity changes of transcription factors by binding association with sorted expression profiles
Background: The identification of transcription factors (TFs) associated with a biological process is fundamental to understanding its regulatory mechanisms. From microarray data,...
Chao Cheng, Xiting Yan, Fengzhu Sun, Lei M. Li
AIIA
2009
Springer
14 years 2 months ago
Ontology-Driven Co-clustering of Gene Expression Data
Abstract. The huge volume of gene expression data produced by microarrays and other high-throughput techniques has encouraged the development of new computational techniques to eva...
Francesca Cordero, Ruggero G. Pensa, Alessia Visco...
CSB
2002
IEEE
169views Bioinformatics» more  CSB 2002»
14 years 18 days ago
Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network. An essential point of Bayesian network con...
Seiya Imoto, SunYong Kim, Takao Goto, Sachiyo Abur...