/DAEi

Autoencoders for Drug-Target Interaction Prediction

Primary LanguagePython

DAEi

Autoencoders for Drug-Target Interaction Prediction

  • Run AEi:

$ python AEi.py --path datasets/ --data_name Enzyme --epoches 300 --batch_size 256 --hidden_size 512 --reg 0.000001 --keep_rate 1.0 --lr 0.001 --min_loss 0.01 --cv 10 --loss_type square --mode dti

  • Run DAEi:

$ python DAEi.py --path datasets/ --data_name Enzyme --epoches 300 --batch_size 256 --hidden_size 512 --regs [0.000001,0.000001,0.000001] --noise_level 0.00001 --lr 0.001 --min_loss 0.01 --cv 10 --loss_type square --mode dti

Parameter description：

  • path：Input data path.
  • data_name：Name of dataset: Enzyme, Ion Channel, GPCR, Nuclear Receptor
  • epoches：Number of epoches.
  • batch_size：Batch size.
  • hidden_size：Hidden layer size, also Embedding size.
  • reg: Regularization for L2.
  • keep_rate: Keep_rate of dropout.
  • lr: Learning rate.
  • min_loss: The minimum value for stopping loss function.
  • cv: K-fold Cross Validation.
  • mode: the mode for training: dti -> drug-target interactions; tdi -> target-drug interactions.