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

We introduce a novel implementation in ANSI C of the MINE family ofalgorithms for computing maximal information-based measures of dependencebetween two variables in large datasets, with the aim of a low memory footprintand ease of integration within bioinformatics pipelines. We provide thelibraries minerva (with the R interface) and minepy for Python, MATLAB, Octaveand C++. The C solution reduces the large memory requirement of the originalJava implementation, has good upscaling properties, and offers a nativeparallelization for the R interface. Low memory requirements are demonstratedon the MINE benchmarks as well as on large (n=1340) microarray and IlluminaGAII RNA-seq transcriptomics datasets.  Availability and Implementation: Source code and binaries are freelyavailable for download under GPL3 licence at http://minepy.sourceforge.net forminepy and through the CRAN repository http://cran.r-project.org for the Rpackage minerva. All software is multiplatform (MS Windows, Linux and OSX).

Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers