合作驱动通路发现有助于研究癌症的发病机制。然而，大多数发现方法主要关注基因组数据，并忽略已知的通路信息和其他相关的多种组学数据；因此，它们不能忠实地解读致癌过程。我们提出了CDPMiner（合作驱动通路挖掘器），通过多重网络嵌入发现合作驱动通路，可以共同建模多种分子的关系和属性信息。CDPMiner首先利用通路拓扑来量化不同通路中基因的权重，并优化基因与通路之间的关系。然后，它构建了一个由微型RNA、长非编码RNA、基因和通路组成的带属性的多重网络，通过深度联合矩阵分解嵌入网络，以挖掘更重要的通路级分析信息并重构通路交互网络。最后，CDPMiner利用重构的网络和突变数据来定义通路之间的驱动器权重，以发现合作驱动通路。乳腺浸润性癌和胃腺癌数据集上的实验结果表明，CDPMiner可以有效地融合多种组学数据以发现更多的驱动通路，这些通路确实合作触发癌症，并对癌症发生机理分析具有价值。消融研究证明CDPMiner能够更全面地分析通过融合多种组学数据的癌症。©作者（们）2023年。由牛津大学出版社出版。版权所有。有关权限，请发送电子邮件至：journals.permissions@oup.com。

Cooperative driver pathways discovery helps researchers to study the pathogenesis of cancer. However, most discovery methods mainly focus on genomics data, and neglect the known pathway information and other related multi-omics data; thus they cannot faithfully decipher the carcinogenic process. We propose CDPMiner (Cooperative Driver Pathways Miner) to discover cooperative driver pathways by multiplex network embedding, which can jointly model relational and attribute information of multi-type molecules. CDPMiner first uses the pathway topology to quantify the weight of genes in different pathways, and optimizes the relations between genes and pathways. Then it constructs an attributed multiplex network consisting of micro RNAs, long noncoding RNAs, genes and pathways, embeds the network through deep joint matrix factorization to mine more essential information for pathway-level analysis and reconstructs the pathway interaction network. Finally, CDPMiner leverages the reconstructed network and mutation data to define the driver weight between pathways to discover cooperative driver pathways. Experimental results on Breast invasive carcinoma and Stomach adenocarcinoma datasets show that CDPMiner can effectively fuse multi-omics data to discover more driver pathways, which indeed cooperatively trigger cancers and are valuable for carcinogenesis analysis. Ablation study justifies CDPMiner for a more comprehensive analysis of cancer by fusing multi-omics data.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.