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» Combined Gene Selection Methods for Microarray Data Analysis
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BMCBI
2007
126views more  BMCBI 2007»
15 years 3 months ago
Including probe-level uncertainty in model-based gene expression clustering
Background: Clustering is an important analysis performed on microarray gene expression data since it groups genes which have similar expression patterns and enables the explorati...
Xuejun Liu, Kevin K. Lin, Bogi Andersen, Magnus Ra...
BMCBI
2006
126views more  BMCBI 2006»
15 years 3 months ago
A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data
Background: As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heteroge...
Zizhen Yao, Walter L. Ruzzo
RECOMB
2001
Springer
16 years 3 months ago
Analysis techniques for microarray time-series data
We address possible limitations of publicly available data sets of yeast gene expression. We study the predictability of known regulators via time-series analysis, and show that l...
Vladimir Filkov, Steven Skiena, Jizu Zhi
117
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BMCBI
2006
131views more  BMCBI 2006»
15 years 3 months ago
The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison
Background: Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, t...
Allan A. Sioson, Shrinivasrao P. Mane, Pinghua Li,...
BIBM
2008
IEEE
108views Bioinformatics» more  BIBM 2008»
15 years 9 months ago
Systematic Evaluation of Scaling Methods for Gene Expression Data
Even after an experimentally prepared gene expression data set has been pre-processed to account for variations in the microarray technology, there may be inconsistencies between ...
Gaurav Pandey, Lakshmi Naarayanan Ramakrishnan, Mi...