Skyler Speakman, Girmaw Abebe Tadesse, et al.
AMIA Annual Symposium 2021
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.
Skyler Speakman, Girmaw Abebe Tadesse, et al.
AMIA Annual Symposium 2021
Venkatesan T. Chakaravarthy, Shivmaran S. Pandian, et al.
SC 2021
Thanh Nguyen, Natasha Mulligan, et al.
AMIA Informatics Summit 2021
Mayuko Ogawa, Genko Oyama, et al.
Parkinsonism and Related Disorders