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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background Biclustering of gene expression data is used to discover groups of genes that are co-expressed over a subset of tested conditions. The objective is to maximize the detection of significant biclusters; to do so, most approaches employ a heuristic approximation in order to avoid a nonpolynomial computational complexity. Previous algorithms have focused on enabling the discovery of biologically relevant results within the scope of single studies, where data size and complexity are limited. New methods and algorithms are required in order to enable applications of biclustering to larger scale data sets that can span multiple experiments and that are potentially far more heterogenous.

Exact biclustering algorithm for the analysis of large gene expression data sets