RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data Expansion

Contact: Junhao Su
Email: [email protected]

Introduction

Relation extraction (RE) is a fundamental task for extracting gene–disease associations from biomedical text. Many state-of-the-art tools have limited capacity, as they can extract gene–disease associations only from single sentences or abstract texts. A few studies have explored extracting gene–disease associations from full-text articles, but there exists a large room for improvements. In this work, we propose RENET2, a deep learning-based RE method, which implements Section Filtering and ambiguous relations modeling to extract gene–disease associations from full-text articles. We designed a novel iterative training data expansion strategy to build an annotated full-text dataset to resolve the scarcity of labels on full-text articles. In our experiments, RENET2 achieved an F1-score of 72.13% for extracting gene–disease associations from an annotated full-text dataset, which was 27.22, 30.30, 29.24 and 23.87% higher than BeFree, DTMiner, BioBERT and RENET, respectively. We applied RENET2 to (i) ∼1.89M full-text articles from PubMed Central and found ∼3.72M gene–disease associations; and (ii) the LitCovid articles and ranked the top 15 proteins associated with COVID-19, supported by recent articles. RENET2 is an efficient and accurate method for full-text gene–disease association extraction. The source-code, manually curated abstract/full-text training data, and results of RENET2 are available at this repo.

RENET2 is published in NAR Genomics and Bioinformatics.

What's new?

20210716

The paper of RENET2 is published. We updated and fixed the empty parsed dataset problem, and updated the parsed full-text dataset in data/ft_data.
20210514

Update README with data link: http://www.bio8.cs.hku.hk/RENET2/renet2_data_models.tar.gz. The full-test annotated dataset is available at /data/ft_info folder in the download files. Please check this link1 and link2 for more detail.

Add RENET testing script for full-text dataset
20210330

We can install RENET2 via bioconda now! and the code for the RENET2 is refined as a python package.

Reference for Application:

Microsoft's BiomedNLP-PubMedBERT, from James Morrill. It achieves an F1 score of 0.8 at the abstract dataset.

Installation

Option 1: Install RENET2 from Bioconda

conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
# create conda environment named "renet2-env"
conda create -n renet2-env -c bioconda renet2
conda activate renet2-env

# run renet2 like this afterwards
renet2 --help

Option 2: Install RENET2 from GitHub

# create renet2 env
conda create -n renet2-env python=3.7
conda activate renet2-env

# install required package
conda install -c conda-forge ruby scikit-learn=0.22.2.post1 pandas=1.0.1 numpy=1.18.1 tqdm=4.42.1
conda install pytorch==1.2.0 cudatoolkit=10.0 -c pytorch

git clone https://github.com/sujunhao/RENET2.git
cd RENET2 
pip install . --no-deps --ignore-installed

# run renet2 like this afterwards
renet2 --help

Download Data and Trained Models

Download all required files

All data and models are available at this link: http://www.bio8.cs.hku.hk/RENET2/renet2_data_models.tar.gz, please using the following scripts to download data for RENET2.

### if RENET2 is installed from Bioconda
mkdir RENET2
cd RENET2
RENET2_DATA_S_URL=https://raw.githubusercontent.com/sujunhao/RENET2/main/src/renet2/download_renet2_data.sh
curl -s ${RENET2_DATA_S_URL} | bash -s .
R2_DIR=$(pwd)

### if RENET2 is installed from GitHub
#### make sure you are in the root dir of RENET2
bash src/renet2/download_renet2_data.sh .
R2_DIR=$(pwd)

Quick test after downloaded all required files

# quick testing
# R2_DIR="[DATA/MODEL_PATH]"                                     # e.g. ~/git/RENET2, check 'Download Data and Trained Models'
renet2 predict --raw_data_dir ${R2_DIR}/data/ft_data/ --gda_fn_d ${R2_DIR}/data/ft_gda/ --models_number 4 --batch_size 8 --max_doc_num 10 --no_cache_file  --model_dir ${R2_DIR}/models/ft_models/

# check predicted results
# predicted gene-disease associations
less ${R2_DIR}/data/ft_gda/gda_rst.tsv

Usage

General usage

# help page for renet2
renet2 --help

# to run a submodule using python
renet2 [submodule] [options]

Setup variables for renet2

R2_DIR="[DATA_MODEL_PATH]"                                     # e.g. ~/git/RENET2, check 'Download Data and Trained Models'

Run RENET2 Model

## for using RENET2, please make sure that -
## 1. in RENET2-env environment (using 'conda activate RENET2-env' to setup RENET2 environment)
## 2. follow the 'Download Data and Trained Models' to download RENET2 dataset and trained models first
## 3. setup the `R2_DIR` variable as in 'Setup variables for renet2'
## use --use_cuda if you have GPUs and want to use GPUs

# set RENET2's models dir, noted that trained model already in this dir
MODEL_DIR=${R2_DIR}/models/ft_models

# train 10 RENET2 models (optional, trained model already in the models dir)
MODEL_DIR=${R2_DIR}/models/ft_models_test
renet2 train --raw_data_dir ${R2_DIR}/data/ft_data/ --annotation_info_dir ${R2_DIR}/data/ft_info --model_dir ${MODEL_DIR} --pretrained_model_p ${R2_DIR}/models/Bst_abs_10  --epochs 10 --models_number 10 --batch_size 60 --have_SiDa ${R2_DIR}/data/ft_info/ft_base/ft_base --gda_fn_d ${R2_DIR}/data/ft_gda/ --use_cuda

# use trained RENET2 models to predict GDAs (using --is_sensitive_mode to enable RENET2-Sensitive mode)
# maximum using 10 models to predict
renet2 predict --raw_data_dir ${R2_DIR}/data/ft_data/ --model_dir ${MODEL_DIR} --models_number 2 --batch_size 60 --gda_fn_d ${R2_DIR}/data/ft_gda/ --use_cuda

# check predicted GDAs
less ${R2_DIR}/data/ft_gda/gda_rst.tsv

# apply 5-fold cross-validation to test RENET2 performance
renet2 evaluate_renet2_ft_cv --epochs 10 --raw_data_dir ${R2_DIR}/data/ft_data/ --annotation_info_dir ${R2_DIR}/data/ft_info/ --rst_file_prefix ft_base --have_SiDa ${R2_DIR}/data/ft_info/ft_base/ft_base --pretrained_model_p ${R2_DIR}/models/Bst_abs_10 --no_cache_file --use_cuda

Pipeline: Use RENET2 to predict Gene-Disease Associations from articles ID

Input: PMID and PMCID list          [example: RENET2/test/test_download_pmcid_list.csv]
Output: Gene-Disease Assoications   [example: will generate at RENET2/data/test_data/gda_rst.tsv]

pipeline with example

Input data: PMID and PMCID list ${R2_DIR}/test/test_download_pmcid_list.csv

download text and NER annotations

# download abstract and its annotations
# (download abstract is required for the full-text case, as some full-text at PTC did not have an abstract section, should download separately)
renet2 download_data --process_n 3 --id_f ${R2_DIR}/test/test_download_pmcid_list.csv --type abs --dir ${R2_DIR}/data/raw_data/abs/ --tmp_hit_f ${R2_DIR}/data/test_data/hit_id_l.csv

# download full-text and its annotations
renet2 download_data --process_n 3 --id_f ${R2_DIR}/test/test_download_pmcid_list.csv --type ft --dir ${R2_DIR}/data/raw_data/ft/ --tmp_hit_f ${R2_DIR}/data/test_data/hit_id_l.csv

parse text and enetities annotations to RENET2 input format

# parse data
renet2 install_geniass          # install geniass, only run one time
conda install ruby              # install ruby
renet2 parse_data --id_f ${R2_DIR}/test/test_download_pmcid_list.csv --type 'ft' --in_abs_dir ${R2_DIR}/data/raw_data/abs/  --in_ft_dir ${R2_DIR}/data/raw_data/ft/ --out_dir ${R2_DIR}/data/test_data/

# normalize NET ID
renet2 normalize_ann  --in_f ${R2_DIR}/data/test_data/anns.txt  --out_f ${R2_DIR}/data/test_data/anns_n.txt

run RENET2 on parsed data

MODEL_DIR=${R2_DIR}/models/ft_models          # using the pretrained 10 models at ft_models
renet2 predict --raw_data_dir ${R2_DIR}/data/test_data/ --model_dir ${R2_DIR}/models/ft_models/ --gda_fn_d ${R2_DIR}/data/test_data/ --models_number 4 --batch_size 8 --max_doc_num 10 --no_cache_file

Output data: predicted Gene-Disease Associations are stored in ${R2_DIR}/data/test_data/gda_rst.tsv

Example of running RENET2 Model on abstract data

to try run RENET2 on abstract, you can using the code as:

renet2 predict --raw_data_dir ${R2_DIR}/data/abs_data/2nd_ann/ \
--model_dir ${R2_DIR}/models/ \
--gda_fn_d ${R2_DIR}/data/test_data/ \
--models_number 1 \
--model_name Bst_abs_10 \
--batch_size 8 \
--no_cache_file \
--fix_snt_n 32 \
--file_name_ann anns.txt

# then go to benchmark folder and run the following to checked the trained models
python calculate_metrics_with_input.py ${R2_DIR}/data/abs_data/2nd_ann/labels.txt ${R2_DIR}/data/test_data/gda_rst.tsv

Understand Output File

There are 7 columns in the gda_rst.tsv:

1	2	3	4	5	6	7
pmid	geneId	diseaseId	g_name	d_name	prob_avg	prob_X

where pmid is Article PubMed Id, geneId is the Entrez Gene ID (Entrez), diseaseId is the Disease Id (MESH), g_name is the gene name (a ID with multiple names will be seperated by '|'), d_name is the disease name (a ID with multiple names is seperated by '|'), prob_avg is the predicted mean GDP (gene-disease probability) of all 10 models, prob_X is the predicted GDP of each models.

Dataset

Parsed Dataset for RENET2

Make sure you downloaded data at the [Download Data and Trained Models] section.

.
├── data                    
│   ├── ft_data             # full-text dataset
│   │   ├── docs.txt        # articles with ID/title/abstract/main text
│   │   ├── sentences.txt   # sentences from articles [collected from geniass]
│   │   ├── anns.txt        # gene/disease annotations [collected from PubTator Central]
│   │   ├── anns_n.txt      # gene/disease annotations with normalize annotated ID 
│   │   ├── labels.txt      # gene-disease assoications table 
│   │   └── s_docs.txt      # articles with section's ID (for visualization of annotated results)
│   ├── abs_data            # abstract dataset
│   │   ├── 1st_ann         # abstract dataset, first round
│   │   │   └── ...                 
│   │   ├── 2nd_ann         # abstract dataset, second round [Abstract-exp in paper]
│   │   │   └── ...                 
│   │   ├── ori             # training dataset from RENET
│   │   │   └── ...                 
│   │   └── ori_test        # testing dataset from RENET
│   │       └── ...                 
│   └── ...                 
└── ...

Annotated Gene-disease Associations Based on Iterative Training Data Expansion

Annotated gene-disease associations based on iterative training data expansion strategy. These are the original annotation files, the parsed files are located at the parsed dataset, please check it accordingly.

.
├── data                    
│   ├── ft_info             
│   │   └── ft_500_n.tsv    # annotated full-text GDA (fisrt and second round)
│   ├── ann_table          
│   │   ├── ann_1st.tsv     # annotated abstract GDA (fisrt round)
│   │   └── ann_2nd.tsv     # annotated abstract GDA (second round)
│   └── ...                 
└── ...

Found Gene-disease Associations from PMC & LitCovid

Make sure you downloaded data at the [Download Data and Trained Models] section.

.
├── data                    
│   ├── pmc 
│   │   └──gda_rst.tsv    # GDA from PMC
│   ├── litcovid 
│   │   └──gda_rst.tsv    # GDA from LitCovid
│   └── ...                 
└── ...

Modules Descriptions

Modules in renet2 are for model training/testing.

For the Modules listed below, please use the -h or --help option for checking available options.

`renet2`	renet2 program
`train`	Module for training RENET2 models.
`predict`	Using RENET2 models to predict gene-disease associations.
`evaluate_renet2_ft_cv`	Evaluating trained RENET2 models and using cross-validation.
`download_data`	Downloading articles from PMC/PTC with provided PMID/PMCID list. (please check example an RENET2/src/nb_scripts/pre_precoss/ for full-text dataset)
`parse_data`	Parsing articles from RENET2. (please check example an RENET2/src/nb_scripts/pre_precoss/ for full-text dataset)
`normalize_ann`	Normlize the annotation ID
`install_geniass`	Install geniass for parse_data module, if fail, please try `conda install ruby` to install ruby first

Benchmark

Run BeFree

pip install pymongo
pip install regex
cd benchmark/BeFree
git clone [email protected]:ibi_group/befree.git

wget http://www.bio8.cs.hku.hk/RENET2/renet2_bm_befree.tar.gz
tar -xf renet2_bm_befree.tar.gz
# get BeFree input
run Generate_BeFree_Input.ipynb on python jypyter notebook to genrate BeFree input
sh benchmark_befree.sh

Run DTMiner

cd benchmark/DTMiner
wget http://www.bio8.cs.hku.hk/RENET2/renet2_bm_dtminer.tar.gz
tar -xf renet2_bm_dtminer.tar.gz
# get DTMiner input
run Generate_DTMiner_Input.ipynb on python jypyter notebook to genrate BeFree input
sh benchmark_DTMiner.sh

Run BioBERT

cd benchmark/BioBERT
git clone https://github.com/dmis-lab/biobert
cd biobert; pip install -r requirements.txt
./download.sh

# generate BioBERT input
run Generate_BioBERT_Input.ipynb on python jypyter notebook

# run BioBERT
sh run_bert.sh

Run RENET (on full-text)

cd benchmark

run Generate_RENET_Input.ipynb on python jypyter notebook

Benchmark (Using RENET2 Cross-validation to Evalutate RENET2/BeFree/DTMiner/BioBERT Results)

renet2 evaluate_renet2_ft_cv --epochs 10 --raw_data_dir ${R2_DIR}/data/ft_data/ --annotation_info_dir ${R2_DIR}/data/ft_info/ --rst_file_prefix ft_base --have_SiDa ${R2_DIR}/data/ft_info/ft_base/ft_base --pretrained_model_p ${R2_DIR}/models/Bst_abs_10 --no_cache_file --use_cuda

Benchmark RENET2 on Abstract Data

# training RENET2 model on abstract data
run ./src/nb_scripts/build_best_model_abs.ipynb on jupyter notebook
# Using cross-validation to benchmarking RENET2 on abstract data
run ./src/nb_scripts/exp_abs.ipynb on jupyter notebook

note that RENET2 can benchmark should be benchmark on abstract data via cross validation.

(Optional) Visualization

Found and visualze a pair of gene-disease annotation obtrained from Pubtar Central.

run ./src/nb_scripts/vis_text.ipynb on jupyter notebook

Name		Name	Last commit message	Last commit date
Latest commit History 42 Commits
benchmark		benchmark
repo_f		repo_f
src		src
.gitignore		.gitignore
LICENSE.md		LICENSE.md
MANIFEST.in		MANIFEST.in
README.md		README.md
setup.py		setup.py

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RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data Expansion

Introduction

Contents

What's new?

Reference for Application:

Installation

Option 1: Install RENET2 from Bioconda

Option 2: Install RENET2 from GitHub

Download Data and Trained Models

Quick test after downloaded all required files

Usage

General usage

Setup variables for renet2

Run RENET2 Model

Pipeline: Use RENET2 to predict Gene-Disease Associations from articles ID

Example of running RENET2 Model on abstract data

Understand Output File

Dataset

Parsed Dataset for RENET2

Annotated Gene-disease Associations Based on Iterative Training Data Expansion

Found Gene-disease Associations from PMC & LitCovid

Modules Descriptions

Benchmark

Run BeFree

Run DTMiner

Run BioBERT

Run RENET (on full-text)

Benchmark (Using RENET2 Cross-validation to Evalutate RENET2/BeFree/DTMiner/BioBERT Results)

Benchmark RENET2 on Abstract Data

(Optional) Visualization

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RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data Expansion

Introduction

Contents

What's new?

Reference for Application:

Installation

Option 1: Install RENET2 from Bioconda

Option 2: Install RENET2 from GitHub

Download Data and Trained Models

Quick test after downloaded all required files

Usage

General usage

Setup variables for renet2

Run RENET2 Model

Pipeline: Use RENET2 to predict Gene-Disease Associations from articles ID

Example of running RENET2 Model on abstract data

Understand Output File

Dataset

Parsed Dataset for RENET2

Annotated Gene-disease Associations Based on Iterative Training Data Expansion

Found Gene-disease Associations from PMC & LitCovid

Modules Descriptions

Benchmark

Run BeFree

Run DTMiner

Run BioBERT

Run RENET (on full-text)

Benchmark (Using RENET2 Cross-validation to Evalutate RENET2/BeFree/DTMiner/BioBERT Results)

Benchmark RENET2 on Abstract Data

(Optional) Visualization

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