Ovarian Cancer Classification using Bayesian Logistic Regression
نویسندگان
چکیده
منابع مشابه
Gene 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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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...
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ژورنال
عنوان ژورنال: IOP Conference Series: Materials Science and Engineering
سال: 2019
ISSN: 1757-899X
DOI: 10.1088/1757-899x/546/5/052049