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.

中文翻译：

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TMO-Net：用于肿瘤学多任务学习的可解释的预训练多组学模型


癌症是一种复杂的疾病，由多种尺度的系统性改变组成。在本研究中，我们开发了肿瘤多组学预训练网络（TMO-Net），该网络集成了多组学泛癌数据集进行模型预训练，促进跨组学交互并实现联合表示学习和不完整组学推理。该模型增强了多组学样本表示，并通过不完整的多组学数据集支持各种下游肿瘤学任务。通过采用可解释的学习，我们描述了不同组学特征对临床结果的贡献。 TMO-Net 模型作为肿瘤学跨模式多组学学习的通用框架，为肿瘤组学特定的基础模型铺平了道路。