Gene selection in cancer classification using sparse logistic regression with Bayesian regularization
نویسندگان
چکیده
منابع مشابه
Gene selection in cancer classification using sparse logistic regression with Bayesian regularization
MOTIVATION Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple but effici...
متن کاملGene Selection in Cancer Classification using Sparse Logistic Regression with Bayesian Regularisation
Motivation: Gene selection algorithms for cancer classification, based on the expression of a small number of biomarker genes, have been the subject of considerable research in recent years. Shevade and Keerthi (2003) propose a gene selection algorithm based on sparse logistic regression (SLogReg) incorporating a Laplace prior to promote sparsity in the model parameters, and provide a simple bu...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2006
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btl386