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

Current methods for viral discovery target evolutionarily conserved proteinsthat accurately identify virus families but remain unable to distinguish thezoonotic potential of newly discovered viruses. Here, we apply anattention-enhanced long-short-term memory (LSTM) deep neural net classifier toa highly conserved viral protein target to predict zoonotic potential acrossbetacoronaviruses. The classifier performs with a 94% accuracy. Analysis andvisualization of attention at the sequence and structure-level featuresindicate possible association between important protein-protein interactionsgoverning viral replication in zoonotic betacoronaviruses and zoonotictransmission.

Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention