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» Combined Gene Selection Methods for Microarray Data Analysis
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114
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ICTAI
2003
IEEE
15 years 8 months ago
Integrating Microarray Data by Consensus Clustering
With the exploding volume of microarray experiments comes increasing interest in mining repositories of such data. Meaningfully combining results from varied experiments on an equ...
Vladimir Filkov, Steven Skiena
123
Voted
IEEEMM
2007
146views more  IEEEMM 2007»
15 years 2 months ago
Learning Microarray Gene Expression Data by Hybrid Discriminant Analysis
— Microarray technology offers a high throughput means to study expression networks and gene regulatory networks in cells. The intrinsic nature of high dimensionality and small s...
Yijuan Lu, Qi Tian, Maribel Sanchez, Jennifer L. N...
147
Voted
BMCBI
2005
152views more  BMCBI 2005»
15 years 2 months ago
CoPub Mapper: mining MEDLINE based on search term co-publication
Background: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to ...
Blaise T. F. Alako, Antoine Veldhoven, Sjozef van ...
136
Voted
BIOINFORMATICS
2005
151views more  BIOINFORMATICS 2005»
15 years 2 months ago
Differential and trajectory methods for time course gene expression data
Motivation: The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differen...
Yulan Liang, Bamidele Tayo, Xueya Cai, Arpad Kelem...
140
Voted
BMCBI
2008
135views more  BMCBI 2008»
15 years 2 months ago
Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data
Background: Normalization is essential in dual-labelled microarray data analysis to remove nonbiological variations and systematic biases. Many normalization methods have been use...
Huiling Xiong, Dapeng Zhang, Christopher J. Martyn...