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» Analysis of variance components in gene expression data
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JIB
2006
110views more  JIB 2006»
14 years 9 months ago
Combining biomedical knowledge and transcriptomic data to extract new knowledge on genes
In biomedical research, interpretation of microarray data requires confrontation of data and knowledge from heterogeneous resources, either in the biomedical domain or in genomics...
Emilie Guérin, Gwenaëlle Marquet, Juli...
98
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BMCBI
2006
109views more  BMCBI 2006»
14 years 9 months ago
Integrated analysis of gene expression by association rules discovery
Background: Microarray technology is generating huge amounts of data about the expression level of thousands of genes, or even whole genomes, across different experimental conditi...
Pedro Carmona-Saez, Monica Chagoyen, Andrés...
BMCBI
2007
112views more  BMCBI 2007»
14 years 9 months ago
Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis
Background: Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene s...
Tae-Min Kim, Yeun-Jun Chung, Mun-Gan Rhyu, Myeong ...
66
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IJCNN
2006
IEEE
15 years 3 months ago
Extraction of Components with Structured Variance
Abstract— We present a method for exploratory data analysis of large spatiotemporal data sets such as global longtime climate measurements, extending our previous work on semibli...
Alexander Ilin, Harri Valpola, Erkki Oja
BIBE
2003
IEEE
128views Bioinformatics» more  BIBE 2003»
15 years 3 months ago
A Repulsive Clustering Algorithm for Gene Expression Data
: - Facing the development of microarray technology, clustering is currently a leading technique to gene expression data analysis. In this paper, we propose a novel algorithm calle...
Chyun-Shin Cheng, Shiuan-Sz Wang