ArticleFrontiers in genetics2024
Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data.
Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.BMC medical imaging · 2026Article
- Triad-LMF: a hierarchical low-rank multimodal fusion framework for robust cancer subtype classification using multi-omics data.BMC bioinformatics · 2026Article
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- GNNMutation: a heterogeneous graph-based framework for cancer detection.BMC bioinformatics · 2025Article
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Authors and funding
6 authors.
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Abstract
Introduction: The combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity. Methods: In this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data. Results: We conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences. Discussion: In the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.
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