SGL-SVM: a novel method for tumor classification via support vector machine with sparse group lasso

DOI10.1016/J.JTBI.2019.110098zbMath1429.92057OpenAlexW2990616444WikidataQ91570999 ScholiaQ91570999MaRDI QIDQ2288505

Yanyan Li

Publication date: 20 January 2020

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2019.110098

zbMATH Keywords

gene expression datafeature selectionsupport vector machinesparse group lassotumor classification

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Pathology, pathophysiology (92C32)

Related Items (4)

A mixed integer linear programming support vector machine for cost-effective group feature selection: branch-cut-and-price approach ⋮ Fer-COCL: A Novel Method Based on Multiple Deep Learning Algorithms for Identifying Fertility-Related Proteins ⋮ Effective dimensionality reduction using kernel locality preserving partial least squares discriminant analysis ⋮ SGL-SVM

Uses Software

• ROCR
• GitHub
• BCDForest
• rL-GenSVM
• boost

Cites Work

• Unnamed Item
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• Better Subset Regression Using the Nonnegative Garrote
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• Model Selection and Estimation in Regression with Grouped Variables
• Use of Ranks in One-Criterion Variance Analysis
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers

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