This is a model for antimicrobial peptides recognition based on BERT which is proposed.
We pre-train a BERT model through amount of proteins sequences downloaded from UniPort.
We fine-tune the model on 4 datasets and evaluate its performance.
We merge all the datasets and train a comprehensive prediction model.
You should first clone the project by command
git clone git@github.com:BioSequenceAnalysis/Bert-Protein.git
Then you need to download models and datasets from the address:
The password is 2aje
Then you should unzip these zips and put them on the root of the project.
You should create data for pre-train by the command
sh create_data.sh
You should ensure the content of file pre_train.sh
input_file is your input data for pre-training whose format is tf_record.
output_dir is the dir of your output model.
bert_config_file defines the structure of the model.
train_batch_size should be change more little if your computer don't support so big batch size.
You can change the num_train_steps by yourself.
After ensuring the content, then you can pre-trian your model by the command:
sh pre_train.sh
When you ready to fine-tune the model or do other, you should run the following code
python ljy_run_classifier.py \
--do_eval False \
--do_save_model True \
--data_name AMPScan \
--batch_size 32 \
--num_train_epochs 50 \
--warmup_proportion 0.1 \
--learning_rate 2e-5 \
--using_tpu False \
--seq_length 128 \
--data_root ./dataset/1kmer_tfrecord/AMPScan/ \
--vocab_file ./vocab/vocab_1kmer.txt \
--init_checkpoint ./model/1kmer_model/model.ckpt \
--bert_config ./bert_config_1.json \
--save_path ./model/AMPScan_1kmer_model/model.ckpt
The meaning of each parameter is as follows, you should change these according to your needs. You can also open file ljy_run_classifier and change the codes in row 16-32 to modify the default values of these parameters.
do_eval: whether to evaluate the model after training
do_save_model: whether to save the model after training
data_name: the name of the training set
batch_size: batch size
num_train_epochs: training epochs
warmup_proportion: proportion of warmup
learning_rate: learning rate
using_tpu: Whether to use TPU
seq_length: sequence length
data_root: the location of the training set to be used
vocab_file: location of dictionary
init_checkpoint: initialization node of the model
bert_config: BERT configuration
save_path: where to save the trained model
You can predict your proteins data by command
python ljy_predict_AMP.py
You should change the codes in row 167-172 according to your needs.
data_name: location of the testing set
out_file: storage location of test results
model_path: the location of the trained model
step: word segmentation
config_file: BERT configuration
vocab_file: location of dictionary