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 explore a rule system and a machine learning (ML) approach to automatically harvest information on gene regulation events (GREs) from biological documents in two different evaluation scenarios -- one uses self-supplied corpora in a clean lab setting, while the other incorporates a standard reference database of curated GREs from RegulonDB, real-life data generated independently from our work. In the lab condition, we test how feasible the automatic extraction of GREs really is and achieve F-scores, under different, not directly comparable test conditions though, for the rule and the ML systems which amount to 34% and 44%, respectively. In the RegulonDB condition, we investigate how robust both methodologies are by comparing them with this routinely used database. Here, the best F-scores for the rule and the ML systems amount to 34% and 19%, respectively.

How feasible and robust is the automatic extraction of gene regulation events?