This package is published alongside a scientific publication describing the development of a human-in-the-loop named entity recognition algorithm specific for protein structures.

Here we provide a number of command line tools to convert annotations found in BioC formatted XML files, as they have been exported from our annotation tool TeamTat (https://www.teamtat.org/), into other formats.

For more details read the documentation here: https://ner-for-protein-structures.readthedocs.io/en/latest/

Clone the repository from the source code on Github:

git clone https://github.com/PDBeurope/ner_for_protein_structures.git

It is good practice to create a virtual environment for development:

python3 -m venv ner_venv

Now activate the venv.

source ner_venv/bin/activate

Next, install all the necessary dependencies using the provided requirements.txt

pip install -r requirements.txt

To be able to use some of the NLP tools install the scientific, English language model

pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.3/en_core_sci_sm-0.5.3.tar.gz

The different available models, their performance stats and download links are given in section "Models" of the documentation. Huggingface supports git and all models can simply be downloaded through "git clone", see example below. However, as the binary file for the models is too large for standard git, the large-file handler needs to be installed in the parent directory the model will be cloned into.

git lfs install

After the large-file handler was installed, the models can be cloned from Huggingface as in the example below.

git clone https://huggingface.co/PDBEurope/Bioformer8L-ProteinStructure-NER-v0.1

Alternatively, the models can be accessed through Huggingface's inference API. This option does require a Huggingface account and an authentication token. The details on how to register and how to set up the token can be found on Huggingface

The annotation handbook with details on how to annotate different entity types and the user guide on the annotation tool TeamTat can be found here: Annotation handbook and user guide

For any feedback, help, bug report please email to: [email protected]

This repository was developed at European Bioinformatics Institute

• Lead Developer: Melanie Vollmar

This project is covered by an MIT license.