Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
Guangzhou National Laboratory, Guangzhou, 510005, China.
Genome Biol. 2024 Jun 6;25(1):149. doi: 10.1186/s13059-024-03293-9.
Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.
癌症是一种复杂的疾病，涉及多个层面的系统性改变。在这项研究中，我们开发了肿瘤多组学预训练网络（TMO-Net），该网络整合了多组学泛癌数据集进行模型预训练，促进了多组学之间的相互作用，并实现了联合表示学习和不完全组学推断。该模型增强了多组学样本的表示能力，并利用不完全的多组学数据集为各种下游肿瘤学任务提供支持。通过采用可解释学习，我们对不同组学特征对临床结局的贡献进行了特征刻画。TMO-Net 模型是肿瘤学中跨模态多组学学习的通用框架，为肿瘤组学专用基础模型铺平了道路。
