IMPROVE

Libraries and scripts for the IMPROVE project.

Purpose

Installation

Clone the IMPROVE library repository to a directory of your preference (outside of your drug response prediction (DRP) model's directory).

git clone https://github.com/JDACS4C-IMPROVE/IMPROVE
cd IMPROVE
git checkout develop

Download data

Download the cross-study analysis (CSA) benchmark data into your model's directory. For example:

./scripts/get-benchmarks $DESTINATION/csa_data/raw_data

The directory structure should look like this after the above steps have been completed:

IMPROVE
DRP_model
└── csa_data

Set environment variables

Specify the full path to the IMPROVE library with $PYTHONPATH and the path to the CSA data with $IMPROVE_DATA_DIR.

cd DRP_model
export PYTHONPATH=$PYTHONPATH:/your/path/to/IMPROVE
export IMPROVE_DATA_DIR="./csa_data/"

IMPROVE repository structure

The improvelib package follows a standard directory structure for organizing its code and resources. Here is a brief overview of the structure:

• src/: This directory contains the source code files for the improvelib package.
• tests/: This directory contains the unit tests for the improvelib package.
• docs/: This directory contains the documentation files for the improvelib package.
• examples/: This directory contains example code and usage scenarios for the improvelib package.
• LICENSE: This file contains the license information for the improvelib package.
• README.md: This file provides an overview and instructions for using the improvelib package.

Please note that this is a general structure and may vary depending on the specific requirements and conventions of the project.

Tutorial

For a detailed guide on how to use the IMPROVE library using an example model, LightGBM, see https://jdacs4c-improve.github.io/docs/content/unified_interface.html.

Examples

Two repositories demonstrating the use of the IMPROVE library for drug response prediction:

• https://github.com/JDACS4C-IMPROVE/GraphDRP/tree/develop -- GraphDRP (deep learning model based on graph neural network)
• https://github.com/JDACS4C-IMPROVE/LGBM/tree/develop -- LightGBM model