ISSEC

ISSEC adopts deep learning to learn specific patterns within predicted inter-residue contacts and subsequently identifies the objects having these patterns as inter-SSE contacts.

Requirements

• Python 2.7.14
• Tensorflow-gpu
• Numpy

Need to do

1. Set your config in ./libs/config/config_v1.py.
2. Specify your raw data path in read_into_tfrecord.py, put your data in the path (Example in ./data/traindata) and run python read_into_tfrecord.py for tfrecord generation.
3. Run python train.py for training, model will be saved in ./output.
4. To test your model on your dataset, you should put the files (.ccmpred, .ss3, .pdb and .fasta) of your dataset in ./data/testdata/<dataset name>/, and run python test.py -m <model path> -d <dataset name> [options]; example that python test.py -m output/new_train_ss3 -d psicov.