SMFM-master

The repository is developed for enhancer sequences identification using Stacked Multivariate Fusion Framework (SMFM)

Requirements

• python == 3.6.2

• torch==1.10.0+cu113

• tensorflow==2.5.1

• transformers==4.12.5

• deep-forest==0.1.5

• Ubuntu 18.04 (64-bit)

Usage

Extract dynamic semantic information using EnhancerBERT

You can extract dynamic semantic information by following a few steps:

• Download EnhancerBERT models and requirement.txt in http://39.104.69.176:5010/.
• Create virtual environment with following command:

1) conda create env -n EnhancerBERT.
2) conda activate EnhancerBERT.
3) pip install requirement.txt.

Revise the path where the EnhancerBERT locate and run extract_information.py After running extract_information.py, the progress of learning implicit relations and long-distance dependicies can be performed by running dl_based_sequence_network.py Finally you can get a .npy file when you have finished the whole process

Generate multi-source biological features

You can generate multi-source biological features by following a few steps:

• Activate the virtual environment described above and run the multi-source_biological_features.py.
• When run the code, you should be aware of the following points:

1) The sequences should be strored as a .fasta or a .txt file.
2) The labels of sequences should be stored independently as a .txt file.
3) After the code has finished running, the features will be store as a .csv file, and please upload the fulldataset instead of optimumdataset.

Predict sequences using dynamic semantic information and multi-source biological features

Run ensemble_classification.py to get the prediction results for the sequences.