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CSB
2004
IEEE
136views Bioinformatics» more  CSB 2004»
13 years 12 months 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
ISMB
2000
13 years 9 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...
JCB
2002
160views more  JCB 2002»
13 years 7 months ago
Inference from Clustering with Application to Gene-Expression Microarrays
There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different u...
Edward R. Dougherty, Junior Barrera, Marcel Brun, ...
BMCBI
2006
173views more  BMCBI 2006»
13 years 8 months ago
Kernel-based distance metric learning for microarray data classification
Background: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with tradit...
Huilin Xiong, Xue-wen Chen
DMKD
2003
ACM
149views Data Mining» more  DMKD 2003»
14 years 1 months ago
Clustering gene expression data in SQL using locally adaptive metrics
Dimitris Papadopoulos, Carlotta Domeniconi, Dimitr...