Ovchinnikova, KatjaKatjaOvchinnikovaBorn, JannisJannisBornChouvardas, PanagiotisPanagiotisChouvardasRapsomaniki, MariannaMariannaRapsomanikiKruithof-de Julio, MariannaMariannaKruithof-de Julio2024-10-262024-10-262024-04-24https://boris-portal.unibe.ch/handle/20.500.12422/176964Machine 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.en600 - Technology::610 - Medicine & healtharticle10.48350/1962203865878510.1038/s41698-024-00583-0