Conference paper
Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi, Judy Hoffman, et al.
NeurIPS 2020
Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personalized prediction models – they focus on absolute effects but do not capture relative differences between cancer subtypes. Our work suggests that using z-scored drug response measures mitigates these limitations and leads to meaningful predictions, opening the door for sophisticated ML precision oncology models.
Baifeng Shi, Judy Hoffman, et al.
NeurIPS 2020
Jihun Yun, Aurelie Lozano, et al.
NeurIPS 2021
Yi Zhou, Parikshit Ram, et al.
ICLR 2023
Dzung Phan, Vinicius Lima
INFORMS 2023