Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data

DOI10.1016/j.csda.2006.06.007zbMath1157.62535OpenAlexW2144705718MaRDI QIDQ1010516

Yongdai Kim, Sunghoon Kwon, Seuck Heun Song

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.06.007

zbMATH Keywords

classificationgene expression datamultinomial logit modelone-against-allsparse logistic regression

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Medical applications (general) (92C50) Biochemistry, molecular biology (92C40)

Related Items (7)

Polarization of forecast densities: a new approach to time series classification ⋮ A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples ⋮ Sparse group Lasso and high dimensional multinomial classification ⋮ Adaptive clustering for time series: application for identifying cell cycle expressed genes ⋮ A modified local quadratic approximation algorithm for penalized optimization problems ⋮ Efficient methods for estimating constrained parameters with applications to regularized (Lasso) logistic regression ⋮ Favorability functions based on kernel density estimation for logistic models: a case study

Uses Software

• LASSO
• SparseLOGREG

Cites Work

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• Better Subset Regression Using the Nonnegative Garrote
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Gene selection for cancer classification using support vector machines

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