A python program that aims to predict protein function prediction using protein-protein interaction networks and go term data.
- Download conda
- Once you have cloned the repo, do the following command to create a conda environment with the necessary packages:
conda env create -f environment.yml- Now you have a conda environment that has all the necessary packages for this project
- To test that everything is working, you can run
python main.py
- main.py: File used to run the algorithms
- neighbor_accuracy: Computes how accurately the neighbors of a GO term can be predicted using random walk
- accuracy_stats.py: Computes stats on neighbor_accuracy
- difference.py: Takes two matched outputs from main and prints a table comparing them
- distribution.py: Visualizes the distribution of GO term neighbor counts
- small_graph.py: Visualizes the impact of pagerank on a directed and undirected graph using a test dataset
- subgraph.py: Visualizes a subgraph of the one built in main based on pagerank node ranks
- paired_sample.py: An additional way to generate pos/neg samples keeping specific aspects constant (then run main with new_random_lists set to False)