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» A Repulsive Clustering Algorithm for Gene Expression Data
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ISBRA
2007
Springer
14 years 3 months ago
Discovering Relations Among GO-Annotated Clusters by Graph Kernel Methods
The biological interpretation of large-scale gene expression data is one of the challenges in current bioinformatics. The state-of-theart approach is to perform clustering and then...
Italo Zoppis, Daniele Merico, Marco Antoniotti, Bu...
BMCBI
2010
167views more  BMCBI 2010»
13 years 9 months ago
Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach
Background: Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle contro...
Marc Bailly-Bechet, Alfredo Braunstein, Andrea Pag...
BIOINFORMATICS
2006
120views more  BIOINFORMATICS 2006»
13 years 9 months ago
Comparison of Affymetrix GeneChip expression measures
Motivation: In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was lar...
Rafael A. Irizarry, Zhijin Wu, Harris A. Jaffee
RECOMB
2010
Springer
14 years 3 months ago
Hierarchical Generative Biclustering for MicroRNA Expression Analysis
Clustering methods are a useful and common first step in gene expression studies, but the results may be hard to interpret. We bring in explicitly an indicator of which genes tie ...
José Caldas, Samuel Kaski
COMPLIFE
2005
Springer
14 years 2 months ago
Robust Perron Cluster Analysis for Various Applications in Computational Life Science
In the present paper we explain the basic ideas of Robust Perron Cluster Analysis (PCCA+) and exemplify the different application areas of this new and powerful method. Recently, ...
Marcus Weber, Susanna Kube