双视图联合学习提升个性化药物协同预测

Dual-view jointly learning improves personalized drug synergy prediction

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BIOINFORMATICS

影响因子:5.4

分区:生物学3区 / 生化研究方法3区生物工程与应用微生物3区数学与计算生物学3区

发表日期:2024 Oct 01

作者: Xueliang Li, Bihan Shen, Fangyoumin Feng, Kunshi Li, Zhixuan Tang, Liangxiao Ma, Hong Li

DOI: 10.1093/bioinformatics/btae604

摘要

准确且稳健的药物组合协同效果预测对精准医学至关重要。尽管已有一些计算方法，但在跨数据集预测中仍存在不可靠性，主要原因在于药物机制复杂和癌症样本异质性。我们提出了JointSyn，通过双视图联合学习预测药物与细胞特征的样本特异性药物作用效果。JointSyn在各项基准测试中均优于现有最先进方法，具有更高的预测准确性和鲁棒性。每个视图捕捉药物协同相关特征并互补贡献于最终药物组合预测。此外，结合微调的JointSyn能提升其对新药物或新癌症样本的泛化能力，只需少量实验测量。我们还利用JointSyn构建了泛癌药物协同的估算图谱，分析不同癌症间的差异模式。这些结果显示，JointSyn具有潜力用于药物协同预测，推动个性化组合疗法的发展。源代码及数据可在https://github.com/LiHongCSBLab/JointSyn获取。

Abstract

Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples.We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and makes complementary contributes to the final prediction of the drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies.Source code and data are available at https://github.com/LiHongCSBLab/JointSyn.