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 Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

The biomedical literature can be seen as a large integrated, but unstructured data repository. Extracting facts from literature and making them accessible is approached from two directions: manual curation efforts develop ontologies and vocabularies to annotate gene products based on statements in papers. Text mining aims to automatically identify entities and their relationships in text using information retrieval and natural language processing techniques. Manual curation is highly accurate but time consuming, and does not scale with the ever increasing growth of literature. Text mining as a high-throughput computational technique scales well, but is error-prone due to the complexity of natural language. How can both be married to combine scalability and accuracy? Here, we review the state-of-the-art text mining approaches that are relevant to annotation and discuss available online services analysing biomedical literature by means of text mining techniques, which could also be utilised by annotation projects. We then examine how far text mining has already been utilised in existing annotation projects and conclude how these techniques could be tightly integrated into the manual annotation process through novel authoring systems to scale-up high-quality manual curation.

Facts from text: can text mining help to scale-up high-quality manual curation of gene products with ontologies?