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GNorm2

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

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

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.