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» Clustering cancer gene expression data: a comparative study
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BMCBI
2005
114views more  BMCBI 2005»
15 years 6 months ago
Signal transduction pathway profiling of individual tumor samples
Background: Signal transduction pathways convey information from the outside of the cell to transcription factors, which in turn regulate gene expression. Our objective is to anal...
Thomas Breslin, Morten Krogh, Carsten Peterson, Ca...
BMCBI
2006
148views more  BMCBI 2006»
15 years 6 months ago
Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of m
Background: The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Mo...
Veronica Vinciotti, Xiaohui Liu, Rolf Turk, Emile ...
BIBE
2007
IEEE
153views Bioinformatics» more  BIBE 2007»
15 years 8 months ago
Combined expression data with missing values and gene interaction network analysis: a Markovian integrated approach
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
Juliette Blanchet, Matthieu Vignes
IJCNN
2008
IEEE
16 years 21 days ago
Ranking and selecting clustering algorithms using a meta-learning approach
Abstract— We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candi...
Marcílio Carlos Pereira de Souto, Ricardo B...
IDA
2005
Springer
15 years 11 months ago
Biological Cluster Validity Indices Based on the Gene Ontology
With the invention of biotechnological high throughput methods like DNA microarrays and the analysis of the resulting huge amounts of biological data, clustering algorithms gain ne...
Nora Speer, Christian Spieth, Andreas Zell