Histopathological imaging‐based cancer heterogeneity analysis via penalized fusion with model averaging

DOI10.1111/BIOM.13357zbMath1520.62223OpenAlexW3080842897WikidataQ98614166 ScholiaQ98614166MaRDI QIDQ6055496

Unnamed Author, Baihua He, Unnamed Author, Qing-Zhao Zhang, Yan Yan Liu, Shuangge Ma

Publication date: 30 October 2023

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.13357

zbMATH Keywords

heterogeneitymodel averagingpenalized fusionhistopathological imaging

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

Hierarchical cancer heterogeneity analysis based on histopathological imaging features ⋮ Regression‐based heterogeneity analysis to identify overlapping subgroup structure in high‐dimensional data

Cites Work

• \(\ell_{1}\)-penalization for mixture regression models
• A weight-relaxed model averaging approach for high-dimensional generalized linear models
• Deviation optimal learning using greedy \(Q\)-aggregation
• Cluster analysis of longitudinal profiles with subgroups
• Structured analysis of the high-dimensional FMR model
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Variable Selection in Finite Mixture of Regression Models
• Finite mixture models
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Sparsity and Smoothness Via the Fused Lasso
• Parsimonious Model Averaging With a Diverging Number of Parameters

This page was built for publication: Histopathological imaging‐based cancer heterogeneity analysis via penalized fusion with model averaging