ArticleFundamental research2026
MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery.
Article in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.Bioinformatics (Oxford, England) · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- An unsupervised method for spatial transcriptomics analysis based on adversarial autoencoder.Briefings in bioinformatics · 2026Article
- AI for biology: Catalyzing interdisciplinary innovation to unravel life's complexity and address biomedical challenges.Fundamental research · 2026Article
- Integrated multi-omics and machine learning identify EFNA3 as a key biomarker of tumor invasion.Frontiers in immunology · 2026Article
- Intra- and inter-multi-omics interaction analysis using deep learning.Bioinformatics advances · 2026Article
- DSCC: disease subtyping using spectral clustering and community detection from consensus networks.Briefings in bioinformatics · 2025Article
- WMRCA + : a weighted majority rule-based clustering method for cancer subtype prediction using metabolic gene sets.Hereditas · 2025Article
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
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7 authors.
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Abstract
Multi-omics cancer data provides complementary views of tumorigenesis and progression. Technical challenges exist in integrating these heterogeneous data into deep learning models to better understand tumorigenesis and predict cancer recurrence. We herein propose a novel end-to-end deep learning method (MULGONET) for cancer recurrence prediction and biomarker discovery. First, MULGONET can effectively solve the curse of dimensionality and the lack of model interpretability in multi-omics data integration. Second, it explores interactions and regulatory relationships between genes and GO terms, thus providing biological insights. Benchmark results show that MULGONET outperforms other contemporary classification methods. It achieves AUPRs of 0.774 ± 0.015, 0.873 ± 0.003 and 0.702 ± 0.011 on the bladder, pancreatic and stomach cancer datasets, respectively. We also show that MULGONET can effectively identify prognostic genes and GO terms associated with cancer recurrence.
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