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» Gene set analysis using principal components
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JIB
2006
110views more  JIB 2006»
13 years 8 months ago
Combining biomedical knowledge and transcriptomic data to extract new knowledge on genes
In biomedical research, interpretation of microarray data requires confrontation of data and knowledge from heterogeneous resources, either in the biomedical domain or in genomics...
Emilie Guérin, Gwenaëlle Marquet, Juli...
ICCV
2003
IEEE
14 years 10 months ago
Shape Representation via Harmonic Embedding
We present a novel representation of shape for closed planar contours explicitly designed to possess a linear structure. This greatly simplifies linear operations such as averagin...
Alessandro Duci, Anthony J. Yezzi, Sanjoy K. Mitte...
BMCBI
2005
120views more  BMCBI 2005»
13 years 8 months ago
SpectralNET - an application for spectral graph analysis and visualization
Background: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks ...
Joshua J. Forman, Paul A. Clemons, Stuart L. Schre...
BMCBI
2008
95views more  BMCBI 2008»
13 years 8 months ago
Gene set analyses for interpreting microarray experiments on prokaryotic organisms
Background: Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Rece...
Nathan L. Tintle, Aaron A. Best, Matthew DeJongh, ...
SDM
2011
SIAM
241views Data Mining» more  SDM 2011»
12 years 11 months ago
A Fast Algorithm for Sparse PCA and a New Sparsity Control Criteria
Sparse principal component analysis (PCA) imposes extra constraints or penalty terms to the standard PCA to achieve sparsity. In this paper, we first introduce an efficient algor...
Yunlong He, Renato Monteiro, Haesun Park