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» Semantic Mining and Analysis of Gene Expression Data
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BMCBI
2006
100views more  BMCBI 2006»
13 years 9 months ago
Chromosomal patterns of gene expression from microarray data: methodology, validation and clinical relevance in gliomas
Background: Expression microarrays represent a powerful technique for the simultaneous investigation of thousands of genes. The evidence that genes are not randomly distributed in...
Federico E. Turkheimer, Federico Roncaroli, Benoit...
TSMC
2008
136views more  TSMC 2008»
13 years 8 months ago
Learning Relational Descriptions of Differentially Expressed Gene Groups
Abstract-- This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to find compactly described groups of genes differen...
Igor Trajkovski, Filip Zelezný, Nada Lavrac...
RECOMB
2001
Springer
14 years 9 months ago
Context-specific Bayesian clustering for gene expression data
The recent growth in genomic data and measurements of genome-wide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. I...
Yoseph Barash, Nir Friedman
BMCBI
2008
178views more  BMCBI 2008»
13 years 9 months ago
Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate vi
Background: The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes ...
Kin-On Cheng, Ngai-Fong Law, Wan-Chi Siu, Alan Wee...
CSDA
2008
128views more  CSDA 2008»
13 years 9 months ago
Assessing agreement of clustering methods with gene expression microarray data
In the rapidly evolving field of genomics, many clustering and classification methods have been developed and employed to explore patterns in gene expression data. Biologists face...
Xueli Liu, Sheng-Chien Lee, George Casella, Gary F...