/EPI-DLMH

Primary LanguagePython

EPI-DLMH

This is our implementation for the paper:

Predicting Enhancer-Promoter Interactions by Deep Learning and Matching Heuristic

Figure1 shows the Architecture of the model. It consists of four steps, including Sequence embedding, Feature extraction, Matching heuristic, and Prediction.

Dataset

Due to size limitation, we put the paper data on Zenodo, available from https://zenodo.org/record/4018229

File Description

  • 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.

  • sequence_processing.py

    Used for pre-processing DNA sequences

  • embedding_matrix.npy

    The weight of the embedding layer converted from the pre-trained DNA vector provided by Ng (2017).

  • train.py, train_c_-.py, train_c_x.py, train_max.py

    Used for training all EPI-DLMH models

  • test.py

    Evaluate the performance of models.

Usage

1.python sequence_processing.py
2.python train.py
3.python test.py