The specification game: rethinking the evaluation of drug response prediction for precision oncology.

ABSTRACT: Precision oncology plays a pivotal role in contemporary healthcare, aiming to optimize treatments for each patient based on their unique characteristics. This objective has spurred the emergence of various cancer cell line drug response datasets, driven by the need to facilitate pre-clinical studies by exploring the impact of multi-omics data on drug response. Despite the proliferation of machine learning models for Drug Response Prediction (DRP), their validation remains critical to reliably assess their usefulness for drug discovery, precision oncology and their actual ability to generalize over the immense space of cancer cells and chemical compounds. Scientific contribution In this paper we show that the commonly used evaluation strategies for DRP methods can be easily fooled by common

SUBMITTER: Codice F 

PROVIDER: S-EPMC11907791 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature