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TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
Author Wang, FA; Zhuang, ZF; Gao, F; He, RK; Zhang, ST; Wang, LS; Liu, JW; Li, YX
Journal GENOME BIOLOGY
Pub Year 2024
Type
Abstract

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.

Issue 25
Volume 25
SCI 10.1