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GECCO
2005
Springer
156views Optimization» more  GECCO 2005»
15 years 5 months ago
Extraction of informative genes from microarray data
Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples...
Topon Kumar Paul, Hitoshi Iba
CORR
2008
Springer
129views Education» more  CORR 2008»
14 years 11 months ago
Hierarchical Bayesian sparse image reconstruction with application to MRFM
This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gau...
Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourne...
BMCBI
2005
153views more  BMCBI 2005»
14 years 11 months ago
A comparative review of estimates of the proportion unchanged genes and the false discovery rate
Background: In the analysis of microarray data one generally produces a vector of p-values that for each gene give the likelihood of obtaining equally strong evidence of change by...
Per Broberg
BMCBI
2008
126views more  BMCBI 2008»
14 years 11 months ago
NITPICK: peak identification for mass spectrometry data
Background: The reliable extraction of features from mass spectra is a fundamental step in the automated analysis of proteomic mass spectrometry (MS) experiments. Results: This co...
Bernhard Y. Renard, Marc Kirchner, Hanno Steen, Ju...
ICML
2004
IEEE
16 years 12 days ago
The multiple multiplicative factor model for collaborative filtering
We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of...
Benjamin M. Marlin, Richard S. Zemel