A nested unsupervised approach to identifying novel molecular subtypes
type: Item
instance of: scholarly article
title: A nested unsupervised approach to identifying novel molecular subtypes
zbMATH Open document ID: 1055.62120
author: Elizabeth S. Garrett
author: Giovanni Parmigiani
published in: Bernoulli
publication date: 2005-02-21T00:00:00Z
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 92C50
Mathematics Subject Classification ID: 92C40
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 2135427
zbMATH Keywords: Bayesian model
zbMATH Keywords: class discovery
zbMATH Keywords: gene expression
zbMATH Keywords: lung cancer
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3150/bj/1106314845
OpenAlex ID: W2133587468
cites work: Q4298912
cites work: How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
cites work: Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations
cites work: A Statistical Framework for Expression-Based Molecular Classification in Cancer
cites work: Design and analysis of DNA microarray investigations.
DOI: 10.3150/BJ/1106314845
description: scientific article; zbMATH DE number 2135427