Reinforcement Learning Complex Detection

This reinforcement learning algorithm is a machine learning method for complex detection in networks. Using known communities, it is trained and learns to find new complexes in the network.

Installation:

Required python3
Requirements installation:
python3 -m pip install -r requirements_py3.txt --user

Experiments:

1. For a toy network use input_toy.yaml
2. For hu.MAP - use input file input_humap.yaml

Instructions:

To run this pipeline on a new network, construct an input file similar to input_toy.yaml specifying where to find the required inputs.

1. Specify input options relating to network: Set options dir_nm (directory containing the network) and netf_nm (file name of the network)
2. Specify input options relating to known communities in network: If you already have sepearated known communities into train and test communitites, specify their paths in the options comf_nm and comf_test_nm (relative to the directory specified in the option:dir_nm) Otherwise, Split complex list into train and test: Set option split_flag = 1 Verify that train test size distributions in figure are the similar. Also check that number of training complexes is not too low by looking at the res_metrics.out file. Set options comf_nm and comf_test_nm with these two files. All the above paths are set relative to the directory specified in the option:dir_nm Make sure to change the option split_flag back to 0 after this step

An example bash script to run the RL pipeline after the above steps is shown below: This is for hu.MAP complexes

#!/bin/bash
mtype = humap
input_file_name = input_$mtype.yaml
graph_file = hu.MAP_network_experiments/input_data/humap_network_weighted_edge_lists.txt
input_training_file = hu.MAP_network_experiments/intermediate_output_results_data/training_CORUM_complexes_node_lists.txt
input_testing_file = hu.MAP_network_experiments/intermediate_output_results_data/testing_CORUM_complexes_node_lists.txt
out_dir_name = /results_$mtype
train_results = $out_dir_name/train_results
pred_results = $out_dir_name/pred_results
id_map_path = convert_ids/humap_gene_id_name_map.txt
echo Training Algorithm....
python3 functions/main_training.py --input_training_file $input_training_file --graph_file $graph_file --train_results $train_results

echo Predicting new complexes from known communities...
python3 functions/main_prediction.py --graph_file $graph_file --train_results $train_results --out_dir_name $out_dir_name --pred_results $pred_results

echo Merging similar communities...
python3 functions/postprocessing.py --input_file_name $input_file_name --graph_file $graph_file --out_dir_name $out_dir_name --pred_results $pred_results --train_results $train_results --input_training_file $input_training_file --input_testing_file $input_testing_file --id_map_path $id_map_path

echo Comparing predicted and known communitites...
python3 functions/eval_complex_RL --input_file_name $input_file_name  --input_training_file $input_training_file --input_testing_file $input_testing_file --out_dir_name $out_dir_name

Additional tips:

For each of the scripts, optional arguments can be viewed by running: python3 script_name.py --help For each command, add the desired argument directly on the terminal.