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» Microarray Gene Expression Data Analysis
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AUSAI
2007
Springer
15 years 1 months ago
Hybrid Methods to Select Informative Gene Sets in Microarray Data Classification
Abstract. One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches
Pengyi Yang, Zili Zhang
BMCBI
2010
110views more  BMCBI 2010»
14 years 9 months ago
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach
Background: One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using mi...
Pietro Zoppoli, Sandro Morganella, Michele Ceccare...
CSB
2005
IEEE
156views Bioinformatics» more  CSB 2005»
15 years 3 months ago
A Robust Meta-classification Strategy for Cancer Diagnosis from Gene Expression Data
One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combi...
Gabriela Alexe, Gyan Bhanot, Babu Venkataraghavan,...
BMCBI
2006
164views more  BMCBI 2006»
14 years 9 months ago
Evaluation of clustering algorithms for gene expression data
Background: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped togethe...
Susmita Datta, Somnath Datta
ALMOB
2008
93views more  ALMOB 2008»
14 years 9 months ago
A weighted average difference method for detecting differentially expressed genes from microarray data
Background: Identification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing whic...
Koji Kadota, Yuji Nakai, Kentaro Shimizu