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RECOMB
2002
Springer
14 years 9 months ago
A new approach to analyzing gene expression time series data
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved timepoints, clustering, and dataset alignment. Each expression p...
Ziv Bar-Joseph, Georg Gerber, David K. Gifford, To...
TCBB
2008
107views more  TCBB 2008»
13 years 8 months ago
Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering
It is a consensus in microarray analysis that identifying potential local patterns, characterized by coherent groups of genes and conditions, may shed light on the discovery of pre...
Hyuk Cho, Inderjit S. Dhillon
IDA
2006
Springer
13 years 8 months ago
Supporting bi-cluster interpretation in 0/1 data by means of local patterns
Clustering or co-clustering techniques have been proved useful in many application domains. A weakness of these techniques remains the poor support for grouping characterization. ...
Ruggero G. Pensa, Céline Robardet, Jean-Fra...
BIOINFORMATICS
2007
137views more  BIOINFORMATICS 2007»
13 years 8 months ago
Annotation-based distance measures for patient subgroup discovery in clinical microarray studies
: Background Clustering algorithms are widely used in the analysis of microarray data. In clinical studies, they are often applied to find groups of co-regulated genes. Clustering...
Claudio Lottaz, Joern Toedling, Rainer Spang
BMCBI
2007
105views more  BMCBI 2007»
13 years 8 months ago
Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor sti
Background: High-throughput experiments, such as with DNA microarrays, typically result in hundreds of genes potentially relevant to the process under study, rendering the interpr...
Rob Jelier, Guido Jenster, Lambert C. J. Dorssers,...