Metadata-Version: 2.1
Name: bent
Version: 0.0.12
Summary: BENT: Biomedical Entity Annotator
Home-page: https://github.com/lasigeBioTM/bent
Author: Pedro Ruas
Author-email: pedrosimruas@gmail.com
License:                                  Apache License
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           Copyright 2023 Pedro Ruas
        
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Keywords: text mining,natural language processing,entity extraction,named entity recognition,named entity linking,normalization,disambiguation,NER,NEL,information extraction
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python
Classifier: Programming Language :: Unix Shell
Classifier: Programming Language :: Java
Classifier: Operating System :: POSIX :: Linux
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.7
License-File: LICENSE.txt



BENT: Biomedical Entity Annotator
---------------------------------

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

NER models are based on `PubMedBERT <https://arxiv.org/pdf/2007.15779.pdf>`__ and a post-processing rule-based module.
the NEL model is a graph-based approach based on the Personalized PageRank algorithm and Information content.

BENT can be used for: 

* Named Entity Recogniton (NER)
* Named Entity Linking (NEL) 
* Named Entity Recognition and Linking (NER+NEL)

Access the full documentation `here <https://bent.readthedocs.io/en/latest/>`__.

Citation:

* Pedro Ruas and Francisco M. Couto. `Nilinker: attention-based approach to nil entity linking <https://www.sciencedirect.com/science/article/pii/S1532046422001526>`__. 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 based on Ubuntu/Debian 
* Conda environment (using `Miniconda <https://docs.conda.io/en/latest/miniconda.html>`__ or `Anaconda <https://docs.conda.io/en/latest/>`__ )
* Python>=3.7
* 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 knowlegde bases for NEL)

.. note::

   Please ensure that you have the appropriate version of Conda installed.


Create a Conda environment (adapt for the name of your project):

::

   conda create --name annotation_project python=3.7


Activate the environment:

::

   conda activate annotation_project


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. Specify the knowledge bases that will be used:

::

   python -c "from bent.setup_package import setup_package;setup_package([<kb1>, <kb2>, <kb3>])"

Available knowledge bases:

* ‘medic’ (`MEDIC <http://ctdbase.org/>`__)

* ‘do’ (`Disease ontology <https://disease-ontology.org/>`__)

* 'chebi’ (`ChEBI ontology <https://www.ebi.ac.uk/chebi/>`__) 

* ‘ctd_chem’ (`CTD-Chemicals <http://ctdbase.org/>`__)

* ‘ncbi_gene’ (`NCBI Gene <https://www.ncbi.nlm.nih.gov/gene/>`__)

* ‘ctd_gene’ (`CTD-GENES <http://ctdbase.org/>`__)

* ‘ncbi_taxon’ (`NCBI Taxonomy <https://www.ncbi.nlm.nih.gov/taxonomy>`__)

* ‘go_bp’ (`Gene Ontology-Biological Process <http://geneontology.org/>`__)

* ‘ctd_anat’ (`CTD-Anatomy <http://ctdbase.org/>`__)

* ‘uberon’ (`UBERON ontology <http://obophenotype.github.io/uberon/>`__)

* ‘go_cc’ (`Gene Ontology-Cellular Component <http://geneontology.org/>`__)

* ‘cell_ontology’ (`Cell Ontology <https://cell-ontology.github.io/>`__)

* cellosaurus’ (`Cellosaurus <https://www.cellosaurus.org/>`__)

Example to download only the MEDIC vocabulary:

::

   python -c "from bent.setup_package import setup_package;setup_package(['medic'])"


If you want to download all knowledge bases, choose the option 'all':

::

   python -c "from bent.setup_package import setup_package;setup_package(['all'])"


You can download more knowledge bases later by running the same command and specifying the desired knolwedge bases among the ones that are available and setting the argument ' only_kb_dicts' to True:

::

   python -c "from bent.setup_package import setup_package;setup_package([<kb>],  only_kb_dicts=True)"


Reinitiate the conda environment.
