yuzhimanhua/Multi-BioNER

Cross-type Biomedical Named Entity Recognition with Deep Multi-task Learning (Bioinformatics'19)

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Emerging

This project helps biomedical researchers, pharmacologists, and biologists automatically identify key biological entities in scientific text. It takes raw biomedical text, such as research abstracts or articles, and highlights specific terms like genes, proteins, chemicals, and diseases within the content. The output is annotated text, making it easier to extract crucial information and insights from large volumes of scientific literature.

135 stars. No commits in the last 6 months.

Use this if you need to accurately and efficiently identify and categorize biological entities like genes, chemicals, and diseases within large collections of biomedical text.

Not ideal if your primary need is general-purpose named entity recognition outside of the biomedical domain, or if you require real-time, low-latency annotation for very short text snippets.

biomedical-research pharmacology bioinformatics literature-mining medical-text-analysis
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 20 / 25

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Stars

135

Forks

27

Language

Python

License

Apache-2.0

Last pushed

Jul 25, 2024

Commits (30d)

0

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