MuSE

MuSE:A Deep Learning Model based on Multi-Feature Fusion for Super-Enhancer Prediction

1. Create one-hot features

Create a directory one hot under the path MuSE/datasets/, next

• python create_onehot.py

1. File descriptions

human_model/mouse_model

data_preparation.py

Data processing for loading X2 features.

run.py

Load the model and the remaining two features, and calculate the evaluation metrics.

model

The trained human and mouse model parameters are saved in this file.

2. Environment setup

We recommend you to build a python virtual environment with Anaconda.

2.1 Create and activate a new virtual environment

conda create -n MuSE python=3.8
conda activate MuSE

2.2 Install the package and other requirements

python -m pip install -r requirements.txt

If you want to download the pytorch environment separately, execute this command

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

3. Calculate evaluation indicators

human/mouse

• python run.py