steventango/GNorm2-docker

GNorm2

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GNorm2 is a gene name recognition and normalization tool with optimized functions and customizable configuration to the user preferences. The GNorm2 integrates multiple deep learning-based methods and achieves state-of-the-art performance. GNorm2 is freely available to download for stand-alone usage. Download GNorm2 here

Content

• Dependency package
• Introduction of folders
• Running GNorm2

Dependency package

The codes have been tested using Python3.8/3.9 on CentOS and uses the following main dependencies on a CPU and GPU:

• TensorFlow 2.3.0
• Transformer 4.18.0
• stanza 1.4.0

To install all dependencies automatically using the command:

$ pip install -r requirements.txt

Introduction of folders

• src_python
  • GeneNER: the codes for gene recognition
  • SpeAss: the codes for species assignment
• src_Java
  • GNormPluslib : the codes for gene normalization and species recogntion
• GeneNER_SpeAss_run.py: the script for runing pipeline
• GNormPlus.jar: the upgraded GNormPlus tools for gene normalization
• gnorm_trained_models:pre-trianed models and trained NER/SA models
  • bioformer-cased-v1.0: the original bioformer model
  • BiomedNLP-PubMedBERT-base-uncased-abstract: the original pubmedbert model
  • geneNER
    • GeneNER-Bioformer/PubmedBERT-Allset.h5: the Gene NER models trained by all datasets
    • GeneNER-Bioformer/PubmedBERT-Trainset.h5: the Gene NER models trained by the training set only
  • SpeAss
    • SpeAss-Bioformer/PubmedBERT-SG-Allset.h5: the Species Assignment models trained by all datasets
    • SpeAss-Bioformer/PubmedBERT-SG-Trainset.h5: the Species Assignment models trained by the trianing set only
  • stanza
    • downloaded stanza library for offline usage
• vocab: label files for the machine learning models of GeneNER and SpeAss
• Dictionary: The dictionary folder contains all required files for gene normalization
• CRF: CRF++ library (called by GNormPlus.sh)
• Library: Ab3P library
• tmp/tmp_GNR/tmp_SA/tmp_SR folders: temp folder
• input/output folders: input and output folders. BioC (abstract or full text) and PubTator (abstract only) formats are both avaliable.
• GNorm2.sh: the script to run GNorm2
• setup.GN.txt/setup.SR.txt/setup.txt the setup files for GNorm2.

Running GNorm2

Please firstly download GNorm2 to your local. Below are the well-trained models (i.e., PubmedBERT/Bioformer) for Gene NER and Species Assignment. Models for Gene NER:

• gnorm_trained_models/geneNER/GeneNER-PubmedBERT.h5
• gnorm_trained_models/geneNER/GeneNER-Bioformer.h5 Models for Species Assignment:
• gnorm_trained_models/SpeAss/SpeAss-PubmedBERT.h5
• gnorm_trained_models/SpeAss/SpeAss-Bioformer.h5

The parameters of the input/output folders:

• INPUT, default="input"
• OUTPUT, default="output"

BioC-XML or PubTator formats are both avaliabel to GNorm2.

1. Run GNorm2

Run Example:

$ ./GNorm2.sh input output

Acknowledgments

This research was supported by the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health.