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» Effective similarity measures for expression profiles
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KDD
2001
ACM
169views Data Mining» more  KDD 2001»
14 years 7 months ago
Hierarchical cluster analysis of SAGE data for cancer profiling
In this paper we present a method for clustering SAGE (Serial Analysis of Gene Expression) data to detect similarities and dissimilarities between different types of cancer on the...
Jörg Sander, Monica C. Sleumer, Raymond T. Ng
BMCBI
2004
158views more  BMCBI 2004»
13 years 7 months ago
Incremental genetic K-means algorithm and its application in gene expression data analysis
Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms suc...
Yi Lu, Shiyong Lu, Farshad Fotouhi, Youping Deng, ...
CVPR
2006
IEEE
14 years 1 months ago
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works...
Xiaoyang Tan, Songcan Chen, Jun Li, Zhi-Hua Zhou
BMCBI
2005
99views more  BMCBI 2005»
13 years 7 months ago
CGH-Profiler: Data mining based on genomic aberration profiles
Background: CGH-Profiler is a program that supports the analysis of genomic aberrations measured by Comparative Genomic Hybridisation (CGH). Comparative genomic hybridisation (CGH...
Falk Schubert, Bernhard Tausch, Stefan Joos, Rolan...
BMCBI
2008
115views more  BMCBI 2008»
13 years 7 months ago
Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient
Background: Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson corre...
Jianchao Yao, Chunqi Chang, Mari L. Salmi, Yeung S...