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» Clustering cancer gene expression data: a comparative study
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GECCO
2005
Springer
146views Optimization» more  GECCO 2005»
15 years 11 months ago
An empirical study of the robustness of two module clustering fitness functions
Two of the attractions of search-based software engineering (SBSE) derive from the nature of the fitness functions used to guide the search. These have proved to be highly robust...
Mark Harman, Stephen Swift, Kiarash Mahdavi
BMCBI
2006
200views more  BMCBI 2006»
15 years 6 months ago
Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data
Background: Numerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, clas...
Ian B. Jeffery, Desmond G. Higgins, Aedín C...
BMCBI
2008
138views more  BMCBI 2008»
15 years 6 months ago
Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles
Background: The hierarchical clustering tree (HCT) with a dendrogram [1] and the singular value decomposition (SVD) with a dimension-reduced representative map [2] are popular met...
Yin-Jing Tien, Yun-Shien Lee, Han-Ming Wu, Chun-Ho...
BMCBI
2008
126views more  BMCBI 2008»
15 years 6 months ago
Relating gene expression data on two-component systems to functional annotations in Escherichia coli
Background: Obtaining physiological insights from microarray experiments requires computational techniques that relate gene expression data to functional information. Traditionall...
Anne M. Denton, Jianfei Wu, Megan K. Townsend, Pre...
BMCBI
2010
95views more  BMCBI 2010»
15 years 6 months ago
Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets
Background: Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this...
Esteban Czwan, Benedikt Brors, David Kipling