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» Bayesian Networks Learning for Gene Expression Datasets
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84
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AIME
2005
Springer
14 years 11 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
IJCNN
2008
IEEE
15 years 3 months ago
Dataset complexity can help to generate accurate ensembles of k-nearest neighbors
— Gene expression based cancer classification using classifier ensembles is the main focus of this work. A new ensemble method is proposed that combines predictions of a small ...
Oleg Okun, Giorgio Valentini
88
Voted
WCE
2007
14 years 10 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
BMCBI
2006
183views more  BMCBI 2006»
14 years 9 months ago
Mining gene expression data by interpreting principal components
Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many insta...
Joseph C. Roden, Brandon W. King, Diane Trout, Ali...
BMCBI
2006
153views more  BMCBI 2006»
14 years 9 months ago
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating v...
Maureen A. Sartor, Craig R. Tomlinson, Scott C. We...