预测抗癌药物反应有助于个性化癌症治疗，是现代肿瘤学研究的一个重要课题。尽管一些方法已用于抗癌药物反应预测，但如何有效地整合与癌细胞系、药物及其已知反应相关的各种特征仍然受到输入特征的冗余信息和特征之间复杂的相互作用的影响。在本研究中，我们提出了一种双线性注意力模型，名为 BANDRP，基于癌细胞系的多个组学数据和药物的多个分子指纹来预测潜在的抗癌药物反应。与现有模型相比，BANDRP利用基因表达数据计算通路富集分数来丰富癌细胞系的特征，并可以通过双线性注意网络自动学习癌细胞系与药物的交互信息。基准测试和独立测试表明，BANDRP 超越了基线模型并表现出强大的泛化性能。消融实验证实了当前模型架构和特征选择方案对于我们的预测任务的最优性。此外，对未知抗癌药物反应预测的分析实验和案例研究强调了 BANDRP 作为预测抗癌药物反应的有效且可靠的框架的潜力。© 作者 2024。由牛津大学出版社出版。

Predicting anti-cancer drug response can help with personalized cancer treatment and is an important topic in modern oncology research. Although some methods have been used for anti-cancer drug response prediction, how to effectively integrate various features related to cancer cell lines, drugs, and their known responses is still affected by the redundant information of input features and the complex interactions between features. In this study, we propose a bilinear attention model, named BANDRP, based on multiple omics data of cancer cell lines and multiple molecular fingerprints of drugs to predict potential anti-cancer drug responses. Compared with existing models, BANDRP uses gene expression data to calculate pathway enrichment scores to enrich the features of cancer cell lines and can automatically learn the interactive information of cancer cell lines and drugs through bilinear attention networks. Benchmarking and independent tests demonstrate that BANDRP surpasses baseline models and exhibits robust generalization performance. Ablation experiments affirm the optimality of the current model architecture and feature selection scheme for our prediction task. Furthermore, analytical experiments and case studies on unknown anti-cancer drug response predictions underscore BANDRP's potential as a potent and reliable framework for predicting anti-cancer drug response.© The Author(s) 2024. Published by Oxford University Press.