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» Gene set analysis using principal components
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BMCBI
2006
116views more  BMCBI 2006»
13 years 8 months ago
Integrative missing value estimation for microarray data
Background: Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performan...
Jianjun Hu, Haifeng Li, Michael S. Waterman, Xiang...
BMCBI
2010
132views more  BMCBI 2010»
13 years 8 months ago
Error margin analysis for feature gene extraction
Background: Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictiv...
Chi Kin Chow, Hai Long Zhu, Jessica Lacy, Winston ...
BMCBI
2010
124views more  BMCBI 2010»
13 years 8 months ago
A factor model to analyze heterogeneity in gene expression
Background: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene d...
Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigu...
ECCV
2004
Springer
14 years 1 months ago
Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors
Diffusion tensor magnetic resonance imaging (DT-MRI) is emerging as an important tool in medical image analysis of the brain. However, relatively little work has been done on produ...
P. Thomas Fletcher, Sarang C. Joshi
IDEAL
2004
Springer
14 years 1 months ago
Dimensionality Reduction with Image Data
A common objective in image analysis is dimensionality reduction. The most common often used data-exploratory technique with this objective is principal component analysis. We pro...
Mónica Benito, Daniel Peña