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/

Zendy Plus

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

Zendy Tools

Zendy Open

Biomedical data sets often have mixed categorical and numerical types, where the former represent semantic information on the objects and the latter represent experimental results. We present the BILCOM algorithm for 'Bi-Level Clustering of Mixed categorical and numerical data types'. BILCOM performs a pseudo-Bayesian process, where the prior is categorical clustering. BILCOM partitions biomedical data sets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations, more accurately than if using one type alone.

Bi-level clustering of mixed categorical and numerical biomedical data