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KDD
2007
ACM
276views Data Mining» more  KDD 2007»
16 years 7 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
BMCBI
2007
177views more  BMCBI 2007»
15 years 6 months ago
The BioPrompt-box: an ontology-based clustering tool for searching in biological databases
Background: High-throughput molecular biology provides new data at an incredible rate, so that the increase in the size of biological databanks is enormous and very rapid. This sc...
Claudio Corsi, Paolo Ferragina, Roberto Marangoni
BMCBI
2005
97views more  BMCBI 2005»
15 years 6 months ago
Clustering the annotation space of proteins
Background: Current protein clustering methods rely on either sequence or functional similarities between proteins, thereby limiting inferences to one of these areas. Results: Her...
Victor Kunin, Christos A. Ouzounis
BMCBI
2005
97views more  BMCBI 2005»
15 years 6 months ago
The yeast kinome displays scale free topology with functional hub clusters
Background: The availability of interaction databases provides an opportunity for researchers to utilize immense amounts of data exclusively in silico. Recently there has been an ...
Robin E. C. Lee, Lynn A. Megeney
BMCBI
2004
208views more  BMCBI 2004»
15 years 6 months ago
Hybrid clustering for microarray image analysis combining intensity and shape features
Background: Image analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identif...
Jörg Rahnenführer, Daniel Bozinov