bent 0.0.59

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>=11/Ubuntu>=20.04

• Python >=3.7, <=3.10.13

• 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.

If you have Docker installed in your system, the easiest way is to pull the BENT Docker image from DockerHub:

::

docker pull pedroruas18/bent

Alternatively, you can install the BENT package using pip:

pip install bent

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>])"

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)

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

Python example:

import bent.annotate as bt

bt.annotate(
        recognize=True,
        link=True,
        types={
         'disease': 'medic'
         'chemical': 'chebi',
         },
        in_dir='data/txt/',
        out_dir='data/ann/'
)

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.