This is a predictor for antimicrobial peptides and specific activities prediction simultaneously, called DMAMP (Deep-learning Model for AntiMicrobial Peptides).
The prediction pipeline uses Python3 and requires the following modules:
- Numpy;
- Keras;
- Tensorflow;
- Sklearn;
- Matplotlib;
Please execute the following code to load the model if you want to predict some antimicrobial peptides.
from keras.models import load_model
model_path = r'model/final_model_710_epoch20_lr0.0001_bc10_task11_2'
model = load_model(model_path, custom_objects={'weight_sample_loss': weight_sample_loss, 'f1_metric':f1_metric, 'recall_metric': recall_metric, 'precision_metric': precision_metric})
bench_878_amp.fa, bench_2405_nonamp.fa: The positive and negative datasets of the training datasets.
pos710.fa, neg710.fa: The positive and negative datasets of the test datasets.
'*.pickle' is the features files in the paper. We used the sixth feature group to realize the work. Thus, please call the sixth feature group in case the feature dimension is mismatching.