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/

Zendy Plus

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

Zendy Tools

Zendy Open

Gene Ontology (GO) is the primary gene function knowledge base that enablescomputational tasks in biomedicine. The basic element of GO is a term, whichincludes a set of genes with the same function. Existing research efforts of GOmainly focus on predicting gene term associations. Other tasks, such asgenerating descriptions of new terms, are rarely pursued. In this paper, wepropose a novel task: GO term description generation. This task aims toautomatically generate a sentence that describes the function of a GO termbelonging to one of the three categories, i.e., molecular function, biologicalprocess, and cellular component. To address this task, we propose aGraph-in-Graph network that can efficiently leverage the structural informationof GO. The proposed network introduces a two-layer graph: the first layer is agraph of GO terms where each node is also a graph (gene graph). Such aGraph-in-Graph network can derive the biological functions of GO terms andgenerate proper descriptions. To validate the effectiveness of the proposednetwork, we build three large-scale benchmark datasets. By incorporating theproposed Graph-in-Graph network, the performances of seven differentsequence-to-sequence models can be substantially boosted across all evaluationmetrics, with up to 34.7%, 14.5%, and 39.1% relative improvements in BLEU,ROUGE-L, and METEOR, respectively.

Graph-in-Graph Network for Automatic Gene Ontology Description Generation