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GECCO
2003
Springer
141views Optimization» more  GECCO 2003»
15 years 3 months ago
Finding the Optimal Gene Order in Displaying Microarray Data
The rapid advances of genome-scale sequencing have brought out the necessity of developing new data processing techniques for enormous genomic data. Microarrays, for example, can g...
Seung-Kyu Lee, Yong-Hyuk Kim, Byung Ro Moon
BMCBI
2010
124views more  BMCBI 2010»
14 years 9 months ago
A factor model to analyze heterogeneity in gene expression
Background: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene d...
Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigu...
BIOINFORMATICS
2005
151views more  BIOINFORMATICS 2005»
14 years 9 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...
CBMS
2005
IEEE
15 years 3 months ago
An Ontology-Driven Clustering Method for Supporting Gene Expression Analysis
The Gene Ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into ...
Haiying Wang, Francisco Azuaje, Olivier Bodenreide...
BMCBI
2007
126views more  BMCBI 2007»
14 years 9 months ago
Including probe-level uncertainty in model-based gene expression clustering
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
Xuejun Liu, Kevin K. Lin, Bogi Andersen, Magnus Ra...