comboKR for predicting drug combination response surfaces
The code is developed with python 3.8.
The main algorithm in comboKR.py is run with numpy 1.23.5, and scikit-learn 1.0.2. The demo depends additionally on some other usual python packages, such as pandas and matplotlib.
Before installing the comboKR package make sure that latest versions of pip and build are installed:
pip3 install --upgrade pip
pip3 install --upgrade build
There are two options for installing the comboKR package.
pip3 install git+https://github.com/aalto-ics-kepaco/comboKR.git#egg=comboKR
mkdir comboKR
cd comboKR
git clone https://github.com/aalto-ics-kepaco/comboKR
After downloading the comboKR package, it can be installed by the following command from the comboKR directory:
pip3 install .
After installation comboKR can be imported as
from comboKR import ComboKR
A small-scale demo is provided in demo.py. Before running it, download and unpack the data_for_demo.zip. The demo runs the experimental setup used in PIICM modification comparison experiments: see supplementary material for details. The expected runtime of the algorithm is as reported there; the full script should run in two minutes.
The algorithm is implemented in the class ComboKR, implementing train and predict -methods. Example on how to use the algorithm can be found from the demo.py.