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