bent 0.0.27

BENT: Biomedical Entity Annotator

Python Library for Named Entity Recognition (NER) and Linking (NEL) in the biomedical domain.

BENT can be used for:

• Named Entity Recogniton (NER)

• Named Entity Linking (NEL)

• Named Entity Recognition and Linking (NER+NEL)

Access the full documentation.

Citation:

Pedro Ruas and Francisco M. Couto. `Nilinker: attention-based approach to nil entity linking.
Journal of Biomedical Informatics, 132:104137, 2022.
doi: https://doi.org/10.1016/j.jbi.2022.104137.

Installation

To use the current version of BENT it is required:

• OS: Debian

• Python 3.7, 3.8 or 3.9

• Required space between 5.5 GB - 10 GB * Dependencies: 2.5 GB * Data: between 3.0 GB (base) or 7.5 GB (if you use all available knowledge bases for Named Entity Linking)

NOTE: Python Docker images (3.7 to 3.9) in Docker Hub have Debian 11 as the base OS.

Install the BENT package using pip:

pip install bent

The following knowledge bases can be configured:

• ‘medic’ (MEDIC)

• ‘do’ (Disease ontology)

• ‘chebi’ (ChEBI ontology)

• ‘ctd_chem’ (CTD-Chemicals)

• ‘ncbi_gene’ (NCBI Gene)

• ‘ctd_gene’ (CTD-GENES)

• ‘ncbi_taxon’ (NCBI Taxonomy)

• ‘go_bp’ (Gene Ontology-Biological Process)

• ‘ctd_anat’ (CTD-Anatomy)

• ‘fma’ (Foundation model of Anatomy)

• ‘uberon’ (UBERON ontology)

• ‘go_cc’ (Gene Ontology-Cellular Component)

• ‘cell_ontology’ (Cell Ontology)

• ‘cellosaurus’ (Cellosaurus)

After the pip installation, it is required a further step to install non-Python dependencies and to download the necessary data. Run in the command line:

bent_setup

Only the default knowledge bases ‘medic’ and ‘chebi’ will be available at this point.

To disable annoyng messages in the terminal run:

export TF_CPP_MIN_LOG_LEVEL='3'

You can download more knowledge bases later by specifying the desired knowledge bases among the ones that are available:

python -c "from bent.get_kbs import get_additional_kbs;get_additional_kbs([<kb1>, <kb2>])"

Example: to download the NCBI Taxonomy and the NCBI Gene run:

::

python -c “from bent.get_kbs import get_additional_kbs;get_additional_kbs([‘ncbi_taxon’, ‘ncbi_gene’])”

Get started

To apply the complete pipeline of entity extraction (NER+NEL) set the arguments:

• recognize: indicate that the NER module will be applied (‘True’)

• link: indicate that the NEL module will be applied (‘True’)

• types: entity types to recognize and the respective target knowledge bases.

• in_dir: directory path containing the text files to be annotated (the directory must contain text files exclusively)

• out_dir: the output directory that will contain the annotation files

Example:

import bent.annotate as bt

bt.annotate(
        recognize=True,
        link=True,
        types={
         'disease': 'medic'
         'chemical': 'chebi',
         'gene': 'ncbi_gene',
         'anatomical': 'uberon',
         'cell_line': 'cellosaurus',
         'bioprocess': 'go_bp'
         },
        in_dir='input/txt/',
        out_dir='output/nel/'
)

It is also possible to apply the pipeline (NER+NEL) to a string or a list or strings instantiated in the execution script.

To see more usage examples, access the documentation.