Benchmarking penalized regression methods in machine learning for single cell RNA sequencing data

DOI10.1007/978-3-031-06220-9_17zbMath1496.92075OpenAlexW4285113094MaRDI QIDQ2163973

Yan Yan, Bhavithry Sen Puliparambil, Jabed Tomal

Publication date: 11 August 2022

Full work available at URL: https://doi.org/10.1007/978-3-031-06220-9_17

zbMATH Keywords

Rmachine learninghigh dimensional datafeature selectionLASSOsingle cell RNA sequencing

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20)

Uses Software

• R
• AS 136
• biglasso
• SGL
• msgl
• HieRFIT
• DropLasso
• seagull

Cites Work

• Unnamed Item
• Algorithm AS 136: A K-Means Clustering Algorithm
• The Adaptive Lasso and Its Oracle Properties
• Sparse group Lasso and high dimensional multinomial classification
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• Sparsity and Smoothness Via the Fused Lasso
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression