Assigning Species Information to Corresponding Genes by a Sequence-Labeling Framework

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This repository contains the source code and dataset for Species Assignment.

Species assignment is the task of identifying the corresponding species of the gene mentions, which is one of the most important steps of gene name normalization. The task of recognizing the corresponding species from various candidates for gene mentions is more relevant to information-retrieval or relation-extraction tasks, but we rephrased the problem into a sequence-labeling task, which is normally applied to a named-entity recognition task. The new method raised the performance accuracy of the species assignment (from 65.8% to 81.3%) within an acceptable process speed for large-scale data processing. Please refer to our paper for more details:

• Ling Luo, Chih-Hsuan Wei, Po-Ting Lai, Qingyu Chen, Rezarta Islamaj, Zhiyong Lu, Assigning species information to corresponding genes by a sequence labeling framework, Database, Volume 2022, 2022, baac090.

Content

• Dependency package
• Introduction of folders
• Download trained models
• Species Assignment with trained models
• Training a new model

Dependency package

The codes have been tested by Python3.8 on CentOS. We used the following main dependencies on GPU (and CPU):

• TensorFlow 2.3.0
• Transformer 4.18.0
• stanza 1.4.0

To install all dependencies automatically, please use the following command:

$ pip install -r requirements.txt

Introduction of folders

• data: the data set for species assignment (training and test sets with PubTator and Conll formats)
• example: input example files, including PubTator and BioC formats
• src: the codes for species assignment
• vocab: label files for machine learning models

Where to download the trained models

To setup the tool on you computer, please download the model file ( it includes two trained models, i.e., PubMedBERT and Bioformer), then uncompress the file, and put the model folder into the SpeciesAssignment folder.

Species Assignment with trained models

Please use our pre-trained models (i.e., PubmedBERT/Bioformer) for species assignment by /src/Species_Assignment.py.

Species_Assignment.py has 3 parameters:

• --input, -i, help="input file"
• --modelfile, -m, help="trained deep learning model file"
• --outpath, -o, help="output folder to save the tagged results"

The input file format should be either BioC(xml) or PubTator(tab-delimited text file). Few examples are provided in the /example/ folder.

Run Example:

$ python Species_Assignment.py -i ../example/Example_input.PubTator -m ../pretrained_models/SpeAss-Bioformer-SG.h5 -o ../results/

Training a new model

User allows to re-train a new model for species assigment. please use /src/Model_Training.py.

Model_Training.py has 4 parameters:

• --trainfile, -t, help="the training set file"
• --devfile, -d, help="the development set file (optional)"
• --modeltype, -m, help="deep learning model (bioformer or pubmedbert?)"
• --outpath, -o, help="the model output folder"

Note that --devfile is an optional parameter. Once the development set is provided, the process can monitor the performance on the development set to determine the timing to stop. If no, the stop time would be determined by the loss of training set.

Run Example:

$ python Model_Training.py -t ../data/SpeAss_Train_token_nonest.conll -m bioformer -o ../models/

Once the training step is finished, the trained model (default: SpeAss-Bioformer-SG-ES-new.h5) with the lowest loss on training set will be automatically generated in the output folder. If the development set is provided, one more model (default: SpeAss-Bioformer-SG-BEST-new.h5) with the highest performance on the development set will be generated in the output folder.

We used the default parameter settings for PubmedBERT/Bioformer and optimized the hyper-parameters as follows: learning rate of 5e-6, batch size of 16. The number of training epochs was chosen by the early stopping strategy, according to the training loss score (100 epochs at most).

Species recognition

We also generated a dictionary-based species tagger that can better handle the enormous size of the species lexicon, based on the hierarchical structure of the taxonomy system. More specifically, our species tagger was implemented by adopting a prefix tree to reorganize the species names within a highly efficient structure for a string search. The species recognition agorithm has been embedded in GNormPlus. Users can follow the instruction to recognize the species entities by the species recognition module.

Acknowledgments

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

Disclaimer

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