This is our implementation for the paper:
Figure1 shows the Architecture of the model. It consists of four steps, including Sequence embedding, Feature extraction, Matching heuristic, and Prediction.Predicting Enhancer-Promoter Interactions by Deep Learning and Matching Heuristic
Due to size limitation, we put the paper data on Zenodo, available from https://zenodo.org/record/4018229
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Data_Augmentation.R
A tool of data augmentation provided by Mao et al. (2017). The details of the tool can be seen in https://github.com/wgmao/EPIANN.
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sequence_processing.py
Used for pre-processing DNA sequences
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embedding_matrix.npy
The weight of the embedding layer converted from the pre-trained DNA vector provided by Ng (2017).
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train.py, train_c_-.py, train_c_x.py, train_max.py
Used for training all EPI-DLMH models
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test.py
Evaluate the performance of models.
1.python sequence_processing.py
2.python train.py
3.python test.py