BioERP
BioERP: a biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions.
Data description
Requirements
BioERP is tested to work under:
- Python 3.6
- Tensorflow 1.1.4
- tflearn
- numpy 1.14.0
- sklearn 0.19.0
Quick start
-
Download the source code of BERT.
-
Manually replace the run_pretraining.py
The network representation model and training regime in BioERP are similar to the original implementation described in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". Therefore, the code of network representation of BioERP can be downloaded from https://github.com/google-research/bert. But BERT uses a combination of two tasks, i.e,. masked language learning and the consecutive sentences classification. Nevertheless, different from natural language modeling, meta paths do not have a consecutive relationship. Therefore, BioERP does not involve the continuous sentences training. If you want to run BioERP, please manually replace the run_pretraining.py and run_classifier.py in BERT with these files.
-
Download the BERT-Base, Uncased model: 12-layer, 768-hidden, 12-heads.
You can construct a vocab file (vocab.txt) of nodes and modify the config file (bert_config.json) which specifies the hyperparameters of the model.
-
Run create_pretraining_data.py to mask metapath sample.
python create_pretraining_data.py \
--input_file=~path/metapath.txt \
--output_file=~path/tf_examples.tfrecord \
--vocab_file=~path/uncased_L-12_H-768_A-12/vocab.txt \
--do_lower_case=True \
--max_seq_length=128 \
--max_predictions_per_seq=20 \
--masked_lm_prob=0.15 \
--random_seed=12345 \
--dupe_factor=5
The max_predictions_per_seq is the maximum number of masked meta path predictions per path sample. masked_lm_prob is the probability for masked token.
- Run run_pretraining.py to train a network representation model based on bio-entity mask mechanism.
python run_pretraining.py \
--input_file=~path/tf_examples.tfrecord \
--output_dir=~path/Local_RLearing_output \
--do_train=True \
--do_eval=True \
--bert_config_file=~path/uncased_L-12_H-768_A-12/bert_config.json \
--train_batch_size=32 \
--max_seq_length=128 \
--max_predictions_per_seq=20 \
--num_train_steps=20000 \
--num_warmup_steps=10 \
--learning_rate=2e-5
- Run run_classifier.py to train a network representation model based on meta path detection mechanism.
python run_classifier.py \
--task_name=CoLA \
--do_train=true \
--do_eval=true \
--data_dir=~path/all_path \
--vocab_file=~path/vocab.txt \
--bert_config_file=~path/bert_config.json \
--max_seq_length=128 \
--train_batch_size=256 \
--learning_rate=2e-5 \
--num_train_epochs=10 \
--output_dir=~path/Global_RLearing_output
- Run extract_features.py extract_features.py to attain the low-dimensional vectors from two representation models.
python extract_features.py \
--input_file=~path/node.txt \
--output_file=~path/output.jsonl \
--vocab_file=~path/uncased_L-12_H-768_A-12/vocab.txt \
--bert_config_file=~path/uncased_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=~path/Local_RLearing_output(or Global_RLearing_output)/model.ckpt \
--layers=-1,-2,-3,-4 \
--max_seq_length=7 \
--batch_size=8
- Run TDI_NeoDTI.py to predict of the confidence scores between targets and drugs for NeoDTI-Net.
python TDI_NeoDTI.py
Please cite our paper if you use this code and data in your work.
@article{BioERP2021,
title = {BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions},
author = {Wang Xiaoqi, and Yang Yaning, and Li Kenli, and Li Wentao, and Li Fei, and Peng Shaoliang},
journal = {Bioinformatics},
year = {2021},
doi = {10.1093/bioinformatics/btab565}
}
Contacts
If you have any questions or comments, please feel free to email: xqw@hnu.edu.cn.