• Usage: the codes can be used to train our models, output results on the testing set, output positional attention weights for chosen group of peptides.

• Please download the full folder of "DeepAttentionPan_Code" to run the programs, or you may need to configure some directory paths.

• Platform: Python 3.6, need to use GPU to train,test and ouput attention weights.

our final ensemble prediction model is stored in the "dup_0" folder, suppose now we want to output testing results of the final model in "dup_0".

1. In the file "dup_0/config.json", the "test_file" refers to the file name of the testing set. All the testing files also need to be stored in the folder "dataset". In the folder "dataset", the "testing_set.txt" is the testing set we used to compare our results with other IEDB benchmark algorithms.
   
2. Back to the directory "codes". Run in the command line:
   "python test.py dup_0/config.json". After the testing process finishing, the testing results will be output to "dup_0", which are "weekly_result.txt" in detail and "weekly_result_METRICS.txt" for final comparison results.

1. Build a new folder "dup_1" inside the folder 'codes'. "dup_1" will be the folder where the algorithm reads the file storing parameters and outputs the trained models.
   
2. Copy the "config.json" in "codes/dup_0" to "dup_1". "config.json" is the file storing useful parameters and the source paths of input data.
   
3. In the file "config.json", change the content of attribute "working_dir" to be "dup_1". You are free to change other parameters to test the effects. The "data_file" refers to the file name of the training set, e.g, "training_set.txt". All the data files should be stored in the folder "dataset".
   
4. Back to the directory "codes". Run in the command line:
   "python train.py dup_1/config.json". After the training process finishing, the networks will be output to "dup_1".

To output the attention weights of certain group of peptides, you can also use the "att_weight.py" from the trained model(e.g. the model stored in the "dup_0").

1. In the "dup_0", change the "test_file" to the name of the file containing the group of the samples that you want to output the average positional attention weights. In the folder "dataset", "testing_9length.txt" contains all the samples with peptides of 9 length in the testing set. And "testing_10length.txt" and "testing_11length.txt" refer to the samples with peptides of 10 length and 11 length correspondently.
   
2. Back to the directory "codes". Run in command line:
   "python att_weight.py dup_0/config.json". The average attention weights of the group will be output to "dup_0".