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» Bayesian Networks Learning for Gene Expression Datasets
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ITSSA
2006
109views more  ITSSA 2006»
14 years 9 months ago
Gene Expression Analysis in Multi-Agent Environment
Abstract. This paper presents a multi-agent approach to gene expression analysis and illustrates the working steps using real dataset produced from a microarray experiment. The ana...
H. C. Lam, M. Vazquez, B. Juneja, Scott C. Fahrenk...
BMCBI
2007
97views more  BMCBI 2007»
14 years 9 months ago
In situ analysis of cross-hybridisation on microarrays and the inference of expression correlation
Background: Microarray co-expression signatures are an important tool for studying gene function and relations between genes. In addition to genuine biological co-expression, corr...
Tineke Casneuf, Yves Van de Peer, Wolfgang Huber
ICANN
2009
Springer
14 years 7 months ago
Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data
Different algorithms have been proposed in the literature to cluster gene expression data, however there is no single algorithm that can be considered the best one independently on...
André C. A. Nascimento, Ricardo Bastos Cava...
ICML
2005
IEEE
15 years 10 months ago
Naive Bayes models for probability estimation
Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating an...
Daniel Lowd, Pedro Domingos
BMCBI
2010
153views more  BMCBI 2010»
14 years 8 months ago
DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules
Background: Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differen...
Bruno M. Tesson, Rainer Breitling, Ritsert C. Jans...