精准肿瘤学的一个主要挑战是根据所考虑肿瘤的分子生物标志物确定合适的治疗方案并确定优先顺序。为了实现这一目标，已成功研究大型癌细胞系组，以阐明细胞特征与治疗反应之间的关系。由于这些数据集的维度较高，因此通常使用机器学习 (ML) 进行分析。然而，选择合适的算法和输入特征集可能具有挑战性。我们对机器学习方法和降维 (DR) 技术进行了全面的基准测试，以预测药物反应指标。利用癌细胞系药物敏感性基因组学，我们为 179 种抗癌化合物训练了随机森林、神经网络、增强树和弹性网，其特征集源自九种 DR 方法。我们比较统计性能、运行时间和可解释性方面的结果。此外，我们还提供了与简单基线模型相比评估模型性能并衡量不同复杂性模型之间权衡的策略。最后，我们表明，复杂的 ML 模型受益于使用优化的 DR 策略，而标准模型（即使使用相当少的特征）仍然可以在性能上表现出色。© 作者 2024。由牛津大学出版社出版。

A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studied to elucidate the relationship between cellular features and treatment response. Due to the high dimensionality of these datasets, machine learning (ML) is commonly used for their analysis. However, choosing a suitable algorithm and set of input features can be challenging. We performed a comprehensive benchmarking of ML methods and dimension reduction (DR) techniques for predicting drug response metrics. Using the Genomics of Drug Sensitivity in Cancer cell line panel, we trained random forests, neural networks, boosting trees and elastic nets for 179 anti-cancer compounds with feature sets derived from nine DR approaches. We compare the results regarding statistical performance, runtime and interpretability. Additionally, we provide strategies for assessing model performance compared with a simple baseline model and measuring the trade-off between models of different complexity. Lastly, we show that complex ML models benefit from using an optimized DR strategy, and that standard models-even when using considerably fewer features-can still be superior in performance.© The Author(s) 2024. Published by Oxford University Press.