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» Redundancy based feature selection for microarray data
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157
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BMCBI
2010
139views more  BMCBI 2010»
15 years 4 months ago
A highly efficient multi-core algorithm for clustering extremely large datasets
Background: In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput t...
Johann M. Kraus, Hans A. Kestler
154
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IDA
2006
Springer
15 years 4 months ago
Backward chaining rule induction
Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe Backward-Chaining Rule Induction (BCR...
Douglas H. Fisher, Mary E. Edgerton, Zhihua Chen, ...
126
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ICDM
2007
IEEE
96views Data Mining» more  ICDM 2007»
15 years 11 months ago
The Chosen Few: On Identifying Valuable Patterns
Constrained pattern mining extracts patterns based on their individual merit. Usually this results in far more patterns than a human expert or a machine learning technique could m...
Björn Bringmann, Albrecht Zimmermann
BMCBI
2008
126views more  BMCBI 2008»
15 years 4 months ago
NITPICK: peak identification for mass spectrometry data
Background: The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments. Results: This co...
Bernhard Y. Renard, Marc Kirchner, Hanno Steen, Ju...
152
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BMCBI
2006
164views more  BMCBI 2006»
15 years 4 months ago
Evaluation of clustering algorithms for gene expression data
Background: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped togethe...
Susmita Datta, Somnath Datta