deepNF

This repository contains Python scripts for "deepNF: Deep network fusion for protein function prediction" by V. Gligorijevic, M. Barot and R. Bonneau.

Citing

@article {Gligorijevic2017,
	author = {Gligorijevi{\'c}, Vladimir and Barot, Meet and Bonneau, Richard},
	title = {deepNF: Deep network fusion for protein function prediction},
	year = {2018},
	doi = {10.1093/bioinformatics/bty440},
        pages = {bty440},
	publisher = {Oxford},
	URL = {http://dx.doi.org/10.1093/bioinformatics/bty440},
	journal = {Bioinformatics}
}

Usage

To run deepNF run the following command from the project directory:

python main.py

To see the list of options:

python main.py --help

To compute network emgeddings only use net_embedding.py script. Input file format: edgelist (i, j, w_ij)

For a single network:

python net_embedding.py --model_type ae --nets example_net_1.txt

For multiple networks:

python net_embedding.py --model_type mda --nets example_net_1.txt example_net_2.txt

Dependencies

deepNF is tested to work under Python 3.6.

The required dependencies for deepNF are Keras, TensorFlow, Numpy, NetworkX and scikit-learn.

Data

Data (PPMI matrices for human and yeast STRING networks as well as protein annotations) used for producing figures in the paper can be downloaded from:

https://users.flatironinstitute.org/vgligorijevic/public_www/deepNF_data/