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JCB
2002
93views more  JCB 2002»
13 years 4 months ago
Unfolding of Microarray Data
The use of DNA microarrays for the analysis of complex biological samples is becoming a mainstream part of biomedical research. One of the most commonly used methods compares the ...
Andrew B. Goryachev, Pascale F. Macgregor, Aled M....
BIRD
2008
Springer
141views Bioinformatics» more  BIRD 2008»
13 years 7 months ago
Nested q-Partial Graphs for Genetic Network Inference from "Small n, Large p" Microarray Data
Abstract. Gaussian graphical models are widely used to tackle the important and challenging problem of inferring genetic regulatory networks from expression data. These models have...
Kevin Kontos, Gianluca Bontempi
JIFS
2002
134views more  JIFS 2002»
13 years 4 months ago
Classification of gene expression data using fuzzy logic
Microarray technologies have allowed the measurement of expression of multiple genes simultaneously. Gene expression levels can be used to classify tissues into diagnostic or progn...
Lucila Ohno-Machado, Staal A. Vinterbo, Griffin We...
BMCBI
2007
97views more  BMCBI 2007»
13 years 5 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
AIME
2005
Springer
13 years 7 months ago
An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset
In recent years, a few researchers have challenged past dogma and suggested methods (such as the IC algorithm) for inferring causal relationship among variables using steady state ...
Xin Zhang, Chitta Baral, Seungchan Kim