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KDD
2004
ACM
237views Data Mining» more  KDD 2004»
16 years 2 months ago
Bayesian Model-Averaging in Unsupervised Learning From Microarray Data
Unsupervised identification of patterns in microarray data has been a productive approach to uncovering relationships between genes and the biological process in which they are in...
Mario Medvedovic, Junhai Guo
93
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BMCBI
2010
98views more  BMCBI 2010»
15 years 1 months ago
A semi-nonparametric mixture model for selecting functionally consistent proteins
Background: High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experim...
Lianbo Yu, R. W. Doerge
BMCBI
2006
103views more  BMCBI 2006»
15 years 1 months ago
Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
Background: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level ...
Sébastien Lemieux
IJBRA
2008
66views more  IJBRA 2008»
15 years 1 months ago
Integer programming-based approach to allocation of reporter genes for cell array analysis
Abstract Observing behaviors of protein pathways and genetic networks under various environments in living cells is essential for unraveling disease and developing drugs. For that ...
Morihiro Hayashida, Fuyan Sun, Sachiyo Aburatani, ...
JMLR
2008
209views more  JMLR 2008»
15 years 1 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger