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- Title
MSFN: a multi-omics stacked fusion network for breast cancer survival prediction.
- Authors
Ge Zhang; Chenwei Ma; Chaokun Yan; Huimin Luo; Jianlin Wang; Wenjuan Liang; Junwei Luo
- Abstract
Introduction: Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge. Methods: In this study, considering the similarity of patients and the relevance of multi-omics data, we propose a novel multi-omics stacked fusion network (MSFN) based on a stacking strategy to predict the survival of breast cancer patients. MSFN first constructs a patient similarity network (PSN) and employs a residual graph neural network (ResGCN) to obtain correlative prognostic information from PSN. Simultaneously, it employs convolutional neural networks (CNNs) to obtain specificity prognostic information from multiomics data. Finally, MSFN stacks the prognostic information from these networks and feeds into AdaboostRF for survival prediction. Results: Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE.
- Subjects
CONVOLUTIONAL neural networks; GRAPH neural networks; DEEP learning; BREAST cancer prognosis; MULTIOMICS; SURVIVAL analysis (Biometry)
- Publication
Frontiers in Genetics, 2024, p1
- ISSN
1664-8021
- Publication type
Article
- DOI
10.3389/fgene.2024.1378809