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» A Repulsive Clustering Algorithm for Gene Expression Data
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ALIFE
2008
13 years 7 months ago
Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data
The development of structure-learning algorithms for gene regulatory networks depends heavily on the availability of synthetic data sets that contain both the original network and ...
Koenraad Van Leemput, Tim Van den Bulcke, Thomas D...
BMCBI
2002
99views more  BMCBI 2002»
13 years 7 months ago
FunSpec: a web-based cluster interpreter for yeast
Background: For effective exposition of biological information, especially with regard to analysis of large-scale data types, researchers need immediate access to multiple categor...
Mark D. Robinson, Jörg Grigull, Naveed Mohamm...
CIDM
2007
IEEE
14 years 2 months ago
Mining Subspace Correlations
— In recent applications of clustering such as gene expression microarray analysis, collaborative filtering, and web mining, object similarity is no longer measured by physical ...
Rave Harpaz, Robert M. Haralick
BMCBI
2008
166views more  BMCBI 2008»
13 years 7 months ago
Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual informa
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
Weijun Luo, Kurt D. Hankenson, Peter J. Woolf
BIOINFORMATICS
2007
190views more  BIOINFORMATICS 2007»
13 years 7 months ago
Towards clustering of incomplete microarray data without the use of imputation
Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by...
Dae-Won Kim, Ki Young Lee, Kwang H. Lee, Doheon Le...