Blocklist expansion with deep learning-based models
Creators: name: Nastou, Katerina
name: Koutrouli, Mikaela
name: Pyysalo, Sampo
name: Jensen, Lars Juhl
Publisher: name: Zenodo
Year: 2024

description: Files in this repository

Tagger related files

dictionary-files-tagger-STRINGv12.zip: this directory contains all the dictionary files used for the tagger runs for all Jensenlab resources (including STRINGv12). It also contains files only relevant for this work, namely: curated_local.tsv & curated_global.tsv which are used for running tagger only with curated blocklists, blacklist_terms_over_10M.txt & blacklist_terms_over_10M+auto_only_list.txt which are used for the automatic blocklist runs only, and empty_global.tsv which is used for the run without blocklists and regex.

tagger-no-regex.tar.gz : this directory contains the Jensenlab tagger with the tagger_core.h header updated so as not to use a regex to block things. This version of the tagger has been used only for the no-blocklist run of the paper. In order to set it up one needs to do the following (for more details check the original repo):



wget https://zenodo.org/api/records/10008720/files/tagger-no-regex.tar.gz

tar -xzvf tagger-no-regex.tar.gz

cd tagger-no-regex

make tagcorpus


 

Training, development and test sets

125k-w100_grid_search_set.tar.gz: A small dataset with 125,000 training and 62,500 development examples used to perform a grid search to detect the best set of hyperparameters

12.5M-w100_train_test_set.tar.gz: A large dataset of 12.5 million training and 62,500 testing examples to train the model used for prediction with the set of best hyperparameters identified above 

 

Transformer-based model

bert-base-finetuned-large-set.tar.gz: this is the TensorFlow model fine-tuned on the large dataset (12.5M) that is used for all the prediction runs. The model is finetuned starting from the BioBERT base v1.1 model. 

 

Prediction runs input generation

The command used to run tagger before running predictions using the bert-base-finetuned-large-set model:



gzip -cd `ls -1 pmc/*.en.merged.filtered.tsv.gz` `ls -1r pubmed/*.tsv.gz` | cat dictionary/excluded_documents.txt - | tagger/tagcorpus --threads=16 --autodetect --types=dictionary/curated_types.tsv --entities=dictionary/all_entities.tsv --names=dictionary/all_names_textmining.tsv --groups=dictionary/all_groups.tsv --stopwords=dictionary/curated_global.tsv --local-stopwords=dictionary/curated_local.tsv --type-pairs=dictionary/all_type_pairs.tsv --out-matches=all_matches.tsv


Input documents for large-scale execution: all PubMed abstracts (as of August 2022) and all full-texts available in the PubmedCentral BioC text mining collection (as of April 2022). The files are converted to a tab-delimited format in order to convert the output to a format compatible with the RE system (see below).

Input dictionary files: all the files necessary to execute the command above are available in dictionary-files-tagger-STRINGv12.zip

Tagger output: we filter the results of the tagger run down to gene or gene products, species, diseases, and chemicals with the process described in our GitHub repository 

 

Blocklist files

curated+auto-blocklists.tar.gz: a combined automatically generated and manually curated blocklist used for the curated+auto runs in the paper. This is the list currently used for the tagger runs for all Jensenlab resources

curated-only-blocklists.tar.gz: the manually curated blocklist used for the curated_only runs in the paper

auto-only-blocklists.tar.gz: the automatically generated blocklist used for the auto_only runs in the paper

 

Supplementary Files

Supplementary Tables.xlsx : A file containing Supplementary Tables 1-9 as mentioned in the manuscript.

Checks to identify score thresholds for Block lists.xlsx: A file containing the lists of names checked to identify proper thresholds for the global/local allow/block lists.

Terms_filtered_from_the_negative_class.txt: this file contains all the terms that were used to filter the negative set of examples if contained within the noun or noun phrase of focus.