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CSB
2003
IEEE
130views Bioinformatics» more  CSB 2003»
14 years 1 months ago
Latent Structure Models for the Analysis of Gene Expression Data
Cluster methods have been successfully applied in gene expression data analysis to address tumor classification. By grouping tissue samples into homogeneous subsets, more systema...
Dong Hua, Dechang Chen, Xiuzhen Cheng, Abdou Youss...
WABI
2005
Springer
14 years 1 months ago
Time-Window Analysis of Developmental Gene Expression Data with Multiple Genetic Backgrounds
d Abstract] Tamir Tuller , Efrat Oron , Erez Makavy , Daniel A. Chamovitz † , and Benny Chor ‡ Tel-Aviv University, Tel-Aviv 69978, Israel. Abstract. We study gene expression d...
Tamir Tuller, Efrat Oron, Erez Makavy, Daniel A. C...
BMCBI
2010
153views more  BMCBI 2010»
13 years 8 months ago
GOAL: A software tool for assessing biological significance of genes groups
Background: Modern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then us...
Alain B. Tchagang, Alexander Gawronski, Hugo B&eac...
BMCBI
2006
142views more  BMCBI 2006»
13 years 8 months ago
Improving the Performance of SVM-RFE to Select Genes in Microarray Data
Background: Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. ...
Yuanyuan Ding, Dawn Wilkins
RECOMB
2007
Springer
14 years 8 months ago
Learning Gene Regulatory Networks via Globally Regularized Risk Minimization
Learning the structure of a gene regulatory network from time-series gene expression data is a significant challenge. Most approaches proposed in the literature to date attempt to ...
Yuhong Guo, Dale Schuurmans