WISER

Cancer drug response prediction through Weak supervISion and supErvised Representation learning

Acknowledgement

Current code base is based on

1. https://github.com/XieResearchGroup/CODE-AE
2. https://github.com/hunterlang/weaksup-subset-selection

Architecture

Overview

Our work introduces a novel representation learning approach that incorporates drug response information during the domain-invariant representation learning phase. We also utilize weak supervision aided by subset selection to efficiently predict drug responses, leveraging patient genomic profiles without documented drug response.

Installation

1. Install anaconda: Instructions here: https://www.anaconda.com/download/
2. pip install -r requirements.txt
3. Download benchmark datasets (CODE-AE) available at Zenodo [http://doi.org/10.5281/zenodo.4776448] (version 2.0)
4. Changed the root dir in the config/data_config.py to the address where benchmark dataset is saved.
5. Run main.py

Configuration

1. In addition to the standard argument-based configuration used in previous work, additional configuration parameters have been provided in config/.