Bnet

Outline

• Set your environment
• Run Bnet
• Main output files
• Bnet container

Set your environment

Download the sources and install according to the following instruction:

Clone the repo from github:

git clone https://github.com/hag007/bnet.git
cd bnet

Bnet is written in Python 3.6. We recommend using a virtual environment. in Linux:

python3 -m venv bnet-env
source bnet-env/bin/activate

To install Bnet dependencies type:

pip install -r  config/dependencies.txt

Run Bnet

Bnet consists of several steps. For a specific set of input parameters, these steps should be carried sequentially.
Each parameter can be specified as command line parameter (For example python script.py --param1 value1 --param2 value2). values of parameters which are not specified in the command line are taken from config/conf.json. Note that all scripts shoudl be executed from the src/emp folder.

1. generate_solution.py: Run the bnet_sa algorithm.
   parameters:
   --dataset_file: path to dataset file.
   --algo: the algorithm to execute.
   --permuted_solutions_folder: folder where permuted solutions reside.
   --true_solutions_folder: folder where true solutions reside.
   --go_folder: folder where GO files are located.
   --network_file: file of the biological network of the analysis.
   --additional_args: additional arguments that are relevant to a particular AMI algorithm.

2. calc_pcs.py: For each module reported in the bnet_sa solution, the first PC is extracted. These PCs will server later as features to train classifiers (see step #3) .
   parameters:
   --dataset_file: path to dataset file.
   --algo: the algoritm to execute.
   --network_file: file of the biological network of the analysis.
   --go_folder: folder where GO files are located.
   --true_solutions_folder: folder where true solutions reside.
   --additional_args: additional arguments that are relevant to a particular AMI algorithm.

3. calc_prediction.py: uses the PCs generated in step #2 as features to train the classifiers RF and SVM. In addition, for each classifier, and check the following metrics: F1, AUPR, AUROC.
   parameters:
   --dataset_file: path to dataset file.
   --algo: AMI algorithm.
   --network_file: file of the biological network of the analysis.
   --go_folder: folder where GO files are located.
   --true_solutions_folder: folder where true solutions reside.
   --additional_args: additional arguments that are relevant to a particular AMI algorithm.

Parameters default values are defined at config/conf.json

Main output files

TBD

Bnet container

Bnet is also available as ready-to-use tool in a container. Using TAU-VPN and gdocker, type gdocker gaga-import --image_name=bnet