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BIBE
2007
IEEE
124views Bioinformatics» more  BIBE 2007»
14 years 1 months ago
Finding Cancer-Related Gene Combinations Using a Molecular Evolutionary Algorithm
—High-throughput data such as microarrays make it possible to investigate the molecular-level mechanism of cancer more efficiently. Computational methods boost the microarray ana...
Chan-Hoon Park, Soo-Jin Kim, Sun Kim, Dong-Yeon Ch...
CSDA
2008
128views more  CSDA 2008»
13 years 7 months ago
Assessing agreement of clustering methods with gene expression microarray data
In the rapidly evolving field of genomics, many clustering and classification methods have been developed and employed to explore patterns in gene expression data. Biologists face...
Xueli Liu, Sheng-Chien Lee, George Casella, Gary F...
BMCBI
2002
195views more  BMCBI 2002»
13 years 7 months ago
Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study
Background: A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expressio...
Junbai Wang, Jan Delabie, Hans Christian Aasheim, ...
BMCBI
2007
127views more  BMCBI 2007»
13 years 7 months ago
A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments
Background: With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across...
Hyungwon Choi, Ronglai Shen, Arul M. Chinnaiyan, D...
RECOMB
2004
Springer
14 years 7 months ago
Predicting Genetic Regulatory Response Using Classification: Yeast Stress Response
We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple orga...
Manuel Middendorf, Anshul Kundaje, Chris Wiggins, ...