TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
In: Genome Biology, Jg. 25 (2024), Heft 1, S. 1-24
Online
academicJournal
Zugriff:
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.
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TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology
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Autor/in / Beteiligte Person: | Wang, Feng-ao ; Zhuang, Zhenfeng ; Gao, Feng ; He, Ruikun ; Zhang, Shaoting ; Wang, Liansheng ; Liu, Junwei ; Li, Yixue |
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Zeitschrift: | Genome Biology, Jg. 25 (2024), Heft 1, S. 1-24 |
Veröffentlichung: | BMC, 2024 |
Medientyp: | academicJournal |
ISSN: | 1474-760X (print) |
DOI: | 10.1186/s13059-024-03293-9 |
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