Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics

DOI10.1515/sagmb-2018-0065zbMath1445.92105arXiv1810.05450OpenAlexW2996006243WikidataQ91916704 ScholiaQ91916704MaRDI QIDQ2195280

Oliver M. Crook, Laurent Gatto, Paul D. W. Kirk

Publication date: 8 September 2020

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.05450

zbMATH Keywords

variable selectionBayesian clusteringcancer proteomics

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40)

Related Items (1)

Collocation based training of neural ordinary differential equations

Uses Software

• mclust
• R
• clustvarsel
• PReMiuM
• VarSelLCM
• sparcl
• sugsvarsel

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Variable selection for model-based clustering using the integrated complete-data likelihood
• Simple approximate MAP inference for Dirichlet processes mixtures
• On a class of Bayesian nonparametric estimates: I. Density estimates
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Estimating the dimension of a model
• Ferguson distributions via Polya urn schemes
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Variable selection methods for model-based clustering
• Prior distributions on spaces of probability measures
• A Bayesian analysis of some nonparametric problems
• Variable selection in clustering via Dirichlet process mixture models
• Variable Selection for Clustering with Gaussian Mixture Models
• Hierarchical Dirichlet Processes
• Estimating Normal Means with a Dirichlet Process Prior
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Bayesian Density Estimation and Inference Using Mixtures
• A Framework for Feature Selection in Clustering
• Bayesian Variable Selection in Clustering High-Dimensional Data
• Variable Selection for Model-Based Clustering
• Improved criteria for clustering based on the posterior similarity matrix
• Variational inference for Dirichlet process mixtures

This page was built for publication: Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics