GENE SELECTION USING LOGISTIC REGRESSIONS BASED ON AIC, BIC AND MDL CRITERIA

DOI10.1142/S179300570500007XzbMath1062.62247OpenAlexW2134021248MaRDI QIDQ4679772

Xiaobo Zhou, Edward R. Dougherty, Xiaodong Wang

Publication date: 21 June 2005

Published in: New Mathematics and Natural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s179300570500007x

zbMATH Keywords

cancer classificationgene microarrayBayesian gene selection

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Genetics and epigenetics (92D10)

Cites Work

• Classification and feature gene selection using the normalized maximum likelihood model for discrete regression
• Estimating the dimension of a model
• Prior Elicitation, Variable Selection and Bayesian Computation for Logistic Regression Models
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Using cluster computers in bioinformatics research
• Gene selection for cancer classification using support vector machines
• A new look at the statistical model identification

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