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BMCBI
2010
144views more  BMCBI 2010»
15 years 6 months ago
Super-sparse principal component analyses for high-throughput genomic data
Background: Principal component analysis (PCA) has gained popularity as a method for the analysis of highdimensional genomic data. However, it is often difficult to interpret the ...
Donghwan Lee, Woojoo Lee, Youngjo Lee, Yudi Pawita...
BMCBI
2010
100views more  BMCBI 2010»
15 years 6 months ago
Efficiency clustering for low-density microarrays and its application to QPCR
Background: Pathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR...
Eric F. Lock, Ryan Ziemiecki, J. S. Marron, Dirk P...
CORR
2010
Springer
210views Education» more  CORR 2010»
15 years 6 months ago
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
Tomer Faktor, Yonina C. Eldar, Michael Elad
BMCBI
2006
119views more  BMCBI 2006»
15 years 6 months ago
Evaluation of microarray data normalization procedures using spike-in experiments
Background: Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-ca...
Patrik Rydén, Henrik Andersson, Mattias Lan...
ISCI
2008
165views more  ISCI 2008»
15 years 6 months ago
Support vector regression from simulation data and few experimental samples
This paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data ...
Gérard Bloch, Fabien Lauer, Guillaume Colin...