Xu Hanwen, Lin Jiacheng, Woicik Addie, Liu Zixuan, Ma Jianzhu, Zhang Sheng, Poon Hoifung, Wang Liewei, Wang Sheng
School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, IL, USA.
Cell Genom. 2025 Jul 9;5(7):100892. doi: 10.1016/j.xgen.2025.100892. Epub 2025 Jun 3.
Drug combination therapy is promising for cancer treatment by reducing resistance and improving efficacy. Machine learning approaches to predicting drug combinations require massive training data. Here, we propose Pisces, a novel machine learning approach for drug combination synergy prediction. The key idea is to augment the sparse dataset by creating multiple views for each drug combination based on different modalities. We combined eight modalities of a drug to create 64 augmented views. By treating each augmented view as a separate instance, Pisces can process any number of drug modalities, circumventing the issue of missing modality. Pisces obtained state-of-the-art results on cell-line-based and xenograft-based drug synergy predictions and drug-drug interaction prediction. By interpreting Pisces's predictions using a genetic interaction network, we identified a breast cancer drug-sensitive pathway from BRCA cell lines. Collectively, the results show that Pisces effectively predicts drug synergy and drug-drug interactions through data augmentation and can be applied to various biological applications.
联合药物疗法通过降低耐药性和提高疗效，在癌症治疗方面前景广阔。预测药物组合的机器学习方法需要大量的训练数据。在此，我们提出了双鱼座（Pisces），一种用于药物组合协同效应预测的新型机器学习方法。其关键思想是通过基于不同模态为每个药物组合创建多个视图来扩充稀疏数据集。我们将一种药物的八种模态组合起来，创建了64个扩充视图。通过将每个扩充视图视为一个单独的实例，双鱼座可以处理任意数量的药物模态，规避了模态缺失的问题。双鱼座在基于细胞系和基于异种移植的药物协同效应预测以及药物 - 药物相互作用预测方面取得了最先进的结果。通过使用遗传相互作用网络解释双鱼座的预测结果，我们从BRCA细胞系中确定了一条乳腺癌药物敏感途径。总体而言，结果表明双鱼座通过数据扩充有效地预测了药物协同效应和药物 - 药物相互作用，并且可以应用于各种生物学应用。
