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CSB
2003
IEEE
130views Bioinformatics» more  CSB 2003»
14 years 2 months ago
Latent Structure Models for the Analysis of Gene Expression Data
Cluster methods have been successfully applied in gene expression data analysis to address tumor classification. By grouping tissue samples into homogeneous subsets, more systema...
Dong Hua, Dechang Chen, Xiuzhen Cheng, Abdou Youss...
BMCBI
2007
135views more  BMCBI 2007»
13 years 9 months ago
Measuring similarities between gene expression profiles through new data transformations
Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is crit...
Kyungpil Kim, Shibo Zhang, Keni Jiang, Li Cai, In-...
ISMB
2000
13 years 10 months ago
Mining for Putative Regulatory Elements in the Yeast Genome Using Gene Expression Data
We have developed a set of methods and tools for automatic discovery of putative regulatory signals in genome sequences. The analysis pipeline consists of gene expression data clu...
Jaak Vilo, Alvis Brazma, Inge Jonassen, Alan J. Ro...
CSB
2004
IEEE
136views Bioinformatics» more  CSB 2004»
14 years 17 days ago
Minimum Entropy Clustering and Applications to Gene Expression Analysis
Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First,...
Haifeng Li, Keshu Zhang, Tao Jiang
BMCBI
2007
126views more  BMCBI 2007»
13 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...