ArticleBMC bioinformatics2023
moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- CancerSubtyper: a deep learning framework for cancer subtyping through DNA methylation data.Epigenetics & chromatin · 2026Article
- DBCL-DFNet: Dual-Branch Contrastive Learning for Multi-Omics Dynamic Fusion.Entropy (Basel, Switzerland) · 2026Article
- A unified framework for correcting batch effects and integrating multi-omics data.Scientific reports · 2026Article
- Domain adaptation, self-supervision, and generative augmentation enhance GNNs for breast cancer prediction.Scientific reports · 2026Article
- Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping.Briefings in functional genomics · 2026Article
- An explainable-AI framework reveals novel lncRNAs specific for breast cancer subtypes.Frontiers in bioinformatics · 2026Article
- GCOA-Net: a graph-regularized cross-omics attention network for interpretable breast cancer molecular subtype classification.Frontiers in medicine · 2026Article
- The role of MiRNA-mediated tumor microenvironment in bone metastasis from a multi-omics perspective: cross-cancer mechanisms and clinical translation.Frontiers in oncology · 2026Review
- Multi-omics driven computational framework for cancer molecular subtype classification.Scientific reports · 2025Article
- Adaptive multi-omics integration framework for breast cancer survival analysis.Scientific reports · 2025Article
- A novel modality contribution confidence-enhanced multimodal deep learning framework for multiomics data.BMC bioinformatics · 2025Article
- MPAC: a computational framework for inferring pathway activities from multi-omic data.Bioinformatics (Oxford, England) · 2025Article
- Bioinformatics Strategies in Breast Cancer Research.Biomolecules · 2025Review
- A densely connected framework for cancer subtype classification.BMC bioinformatics · 2025Article
- IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.Bioinformatics (Oxford, England) · 2025Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data.Bioinformatics advances · 2025Article
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
- Article
- A comparative analysis of gene expression profiling by statistical and machine learning approaches.Bioinformatics advances · 2025Article
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2 authors.
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Abstract
backgroundBreast cancer is a highly heterogeneous disease that comprises multiple biological components. Owing its diversity, patients have different prognostic outcomes; hence, early diagnosis and accurate subtype prediction are critical for treatment. Standardized breast cancer subtyping systems, mainly based on single-omics datasets, have been developed to ensure proper treatment in a systematic manner. Recently, multi-omics data integration has attracted attention to provide a comprehensive view of patients but poses a challenge due to the high dimensionality. In recent years, deep learning-based approaches have been proposed, but they still present several limitations.
resultsIn this study, we describe moBRCA-net, an interpretable deep learning-based breast cancer subtype classification framework that uses multi-omics datasets. Three omics datasets comprising gene expression, DNA methylation and microRNA expression data were integrated while considering the biological relationships among them, and a self-attention module was applied to each omics dataset to capture the relative importance of each feature. The features were then transformed to new representations considering the respective learned importance, allowing moBRCA-net to predict the subtype.
conclusionsExperimental results confirmed that moBRCA-net has a significantly enhanced performance compared with other methods, and the effectiveness of multi-omics integration and omics-level attention were identified. moBRCA-net is publicly available at https://github.com/cbi-bioinfo/moBRCA-net .
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