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» A cluster validity framework for genome expression data
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ICASSP
2011
IEEE
13 years 1 months ago
Joint modeling of observed inter-arrival times and waveform data with multiple hidden states for neural spike-sorting
We present a novel, maximum likelihood framework for automatic spike-sorting based on a joint statistical model of action potential waveform shape and inter-spike interval duratio...
Brett Matthews, Mark Clements
RECOMB
2002
Springer
14 years 10 months ago
Probabilistic hierarchical clustering for biological data
Biological data, such as gene expression profiles or protein sequences, is often organized in a hierarchy of classes, where the instances assigned to "nearby" classes in...
Eran Segal, Daphne Koller
BMCBI
2005
112views more  BMCBI 2005»
13 years 9 months ago
Visualization methods for statistical analysis of microarray clusters
Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determin...
Matthew A. Hibbs, Nathaniel C. Dirksen, Kai Li, Ol...
BMCBI
2006
203views more  BMCBI 2006»
13 years 9 months ago
Independent component analysis reveals new and biologically significant structures in micro array data
Background: An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem...
Attila Frigyesi, Srinivas Veerla, David Lindgren, ...
BMCBI
2010
125views more  BMCBI 2010»
13 years 10 months ago
NeatMap - non-clustering heat map alternatives in R
Background: The clustered heat map is the most popular means of visualizing genomic data. It compactly displays a large amount of data in an intuitive format that facilitates the ...
Satwik Rajaram, Yoshi Oono