4.2.4 DenseLSTM - WangLabTHU/GPro GitHub Wiki
hcwang and qxdu edited on Aug 4, 2023, 1 version
DenseLSTM is a predictive model for designing flanking sequence in mammalian cells[1]. With this model, researchers have designed E. coli constitutive, isopropyl-beta-D-1-thiogalactopyranoside (IPTG)-inducible, and mammalian cell doxycycline (Dox)-inducible promoters with significant performance improvements.A schematic diagram of the whole workflow has been provided.
Here, in order to facilitate users' understanding of the process of the model in biological sequences, we provide a more detailed operational pipeline.
Caution: We recommend Input sequences should have a length exceeding 100bp, as we found that the two models related to densenet have limited performance on short sequences.
All parameters should be defined during the initialization phase. We have encapsulated the source code, thus all predictive models have unified input and output parameters. There are two types of parameters, one should be defined during the initialization phase (Initialization), and the other should be defined during the training/sampling phase (Training/Predicting).
| params | description | default value |
|---|---|---|
| batch_size | training batch size | 64 |
| length | sequential length of the training dataset | 50 |
| model_name | parameter that controls the saving path under "./checkpoints" | denselstm |
| epoch | training epochs | 200 |
| patience | earlystopping when the indicators no longer change | 50 |
| log_steps | logging the output/criterias of the model every print_epoch epochs | 10 |
| save_steps | saving the result of model every save_epoch epochs | 20 |
| exp_mode | the processing mode for expression input | log2 |
| params | description | default value | flexible stage |
|---|---|---|---|
| dataset | training dataset sequences path, fasta file | None | train() |
| labels | training dataset expression path, txt file, each line an expression corresponding to dataset | None | train() |
| savepath | final model saving path directory | None | train() |
| model_path | model loading directory | None |
predict()/predict_input()
|
| data_path | dataset to be predicted , fasta file | None | predict() |
| inputs | data for predict_input, can be datapath, sequence list or onehot encoded data | None | predict_input() |
| mode | input mode for predict_input, can be "path","data" or "onehot" | "path" | predict_input() |
Caution: predict() function will directly generate samples in checkpoint path, but predict_input() will not generate the file automatically.
A demo for model training/predicting is described below:
from gpro.predictor.denselstm.denselstm import DenseLSTM_language
model = DenseLSTM_language(length=50, epoch=400, patience=10, exp_mode="direct")
# Train
default_root = "your working directory"
dataset = os.path.join(default_root, 'data/seq.txt')
labels = os.path.join(default_root, 'data/exp.txt')
save_path = os.path.join(default_root, 'checkpoints/')
model.train(dataset=dataset,labels=labels,savepath=save_path)
# Predict
model_path = os.path.join(default_root, "checkpoints/denselstm/checkpoint.pth")
data_path = os.path.join(default_root, "data/example.txt")
model.predict(model_path=model_path, data_path=data_path)
# Predict input
res = model.predict_input(model_path=model_path, inputs=data_path)
print(res)[1] Zhang P, Wang H, Xu H, et al. Deep flanking sequence engineering for efficient promoter design[J]. bioRxiv, 2023: 2023.04. 14.536502.