English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In classification problems,arising in genomics,research it is common,to study populations for which a broad class assignment is known (say, normal versus diseased) and one seeks to find undiscovered,subclasses within one or both of the known classes. Formally, this problem can be thought of as an unsupervised analysis nested within a supervised one. Here we take the view that the nested unsupervised analysis can successfully utilize information from the entire data set for constructing and/or selecting useful predictors. Specifically, we propose a mixture model approach to the nested unsupervised problem, where the super- vised information is used to develop latent classes which are in turn used for data mining and robust unsupervised analysis. Our solution is illustrated using data on molecularclassification of lung adenocarcinoma. A nested unsupervised approach to identifying novel molecular subtypes Elizabeth S. Garrett & Giovanni Parmigiani Sidney Kimmel Comprehensive Cancer Center