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CBMS
2005
IEEE
13 years 10 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
173views more  BMCBI 2007»
13 years 5 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
BMCBI
2008
114views more  BMCBI 2008»
13 years 5 months ago
Partial mixture model for tight clustering of gene expression time-course
Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively l...
Yinyin Yuan, Chang-Tsun Li, Roland Wilson
BIBM
2008
IEEE
212views Bioinformatics» more  BIBM 2008»
13 years 11 months ago
Analysis of Multiplex Gene Expression Maps Obtained by Voxelation
Background: Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelatio...
Li An, Hongbo Xie, Mark H. Chin, Zoran Obradovic, ...
TCBB
2010
136views more  TCBB 2010»
13 years 3 months ago
Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
— The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses ...
Oliver Rübel, Gunther H. Weber, Min-Yu Huang,...