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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Nowadays there is a big spotlight cast on the development of techniques ofexplainable machine learning. Here we introduce a new computational paradigmbased on Group Equivariant Non-Expansive Operators, that can be regarded as theproduct of a rising mathematical theory of information-processing observers.This approach, that can be adjusted to different situations, may have manyadvantages over other common tools, like Neural Networks, such as: knowledgeinjection and information engineering, selection of relevant features, smallnumber of parameters and higher transparency. We chose to test our method,called GENEOnet, on a key problem in drug design: detecting pockets on thesurface of proteins that can host ligands. Experimental results confirmed thatour method works well even with a quite small training set, providing thus agreat computational advantage, while the final comparison with otherstate-of-the-art methods shows that GENEOnet provides better or comparableresults in terms of accuracy.

GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection