癌症驱动基因的鉴定对于癌症的早期发现、有效治疗和精准医疗至关重要。癌症是由多个基因在不同调节水平上的失调引起的。然而，当前的技术只能捕获有限数量的监管信息，这可能会阻碍其有效性。在本研究中，我们提出了 IMI-driver 模型，该模型将多组学数据集成到八个生物网络中，并应用多视图协作网络嵌入将生物网络中的基因调控信息嵌入到低维向量空间中以识别癌症司机。我们将 IMI 驱动程序应用于癌症基因组图谱 (TCGA) 中的 29 种癌症类型，并在 9 个基准数据集上将其性能与其他 9 种方法进行比较。 IMI-driver 优于其他方法，证明多级网络集成提高了预测准确性。我们还使用 IMI-driver 识别的基因进行泛癌分析，这证实了我们选择的几乎所有候选基因都是已知或潜在的驱动基因。新阳性基因的案例研究表明它们在癌症发生和进展中的作用。版权所有：© 2024 Shi et al.这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression.Copyright: © 2024 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.