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BMCBI
2010
135views more  BMCBI 2010»
13 years 6 months ago
Simple and flexible classification of gene expression microarrays via Swirls and Ripples
Background: A simple classification rule with few genes and parameters is desirable when applying a classification rule to new data. One popular simple classification rule, diagon...
Stuart G. Baker
BMCBI
2007
194views more  BMCBI 2007»
13 years 6 months ago
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Xin Zhao, Leo Wang-Kit Cheung
BMCBI
2008
128views more  BMCBI 2008»
13 years 6 months ago
Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate reg
Background: Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good...
David D. Smith, Pål Sætrom, Ola R. Sn&...
BMCBI
2010
190views more  BMCBI 2010»
13 years 6 months ago
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification alg
Background: Data generated using `omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of...
Yu Guo, Armin Graber, Robert N. McBurney, Raji Bal...
ISMIS
2003
Springer
13 years 11 months ago
Evolutionary Computation for Optimal Ensemble Classifier in Lymphoma Cancer Classification
Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with ...
Chanho Park, Sung-Bae Cho