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

Nanopore sensing is a versatile technique for the analysis of molecules onthe single-molecule level. However, extracting information from data withestablished algorithms usually requires time-consuming checks by an experiencedresearcher due to inherent variability of solid-state nanopores. Here, wedevelop a convolutional neural network (CNN) for the fully automated extractionof information from the time-series signals obtained by nanopore sensors. Inour demonstration, we use a previously published dataset on multiplexedsingle-molecule protein sensing. The neural network learns to classifytranslocation events with greater accuracy than previously possible, while alsoincreasing the number of analysable events by a factor of five. Our resultsdemonstrate that deep learning can achieve significant improvements in singlemolecule nanopore detection with potential applications in rapid diagnostics.

QuipuNet: convolutional neural network for single-molecule nanopore sensing