ArticleBioinformatics (Oxford, England)2023
Interpretable meta-learning of multi-omics data for survival analysis and pathway enrichment.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A pan-cancer multi-omicFrontiers in artificial intelligence · 2026Article
- Interpretable Machine Learning for Survival Analysis.Biometrical journal. Biometrische Zeitschrift · 2025Review
- Exploring the Role of Transcriptomics, Proteomics, and Machine Learning in HPV Infection and Cardiovascular Disease.Biomedicines · 2025Review
- Multi-Omics Feature Selection to Identify Biomarkers for Hepatocellular Carcinoma.Metabolites · 2025Article
- Integrative Analysis of Multi-Omics Data for Biomarker Discovery.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Survival prediction from imbalanced colorectal cancer dataset using hybrid sampling methods and tree-based classifiers.Scientific reports · 2025Article
- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction.Briefings in bioinformatics · 2025Article
- Federated transfer learning with differential privacy for multi-omics survival analysis.Briefings in bioinformatics · 2025Article
- A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables.Briefings in bioinformatics · 2025Review
- Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer.Nature communications · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Computational models for pan-cancer classification based on multi-omics data.Frontiers in genetics · 2025Review
- MMOSurv: meta-learning for few-shot survival analysis with multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- Single-cell transcriptome analysis revealed heterogeneity in glycolysis and identified IGF2 as a therapeutic target for ovarian cancer subtypes.BMC cancer · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.Briefings in bioinformatics · 2024Article
- Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma.Frontiers in genetics · 2024Article
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5 authors.
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Abstract
motivationDespite the success of recent machine learning algorithms' applications to survival analysis, their black-box nature hinders interpretability, which is arguably the most important aspect. Similarly, multi-omics data integration for survival analysis is often constrained by the underlying relationships and correlations that are rarely well understood. The goal of this work is to alleviate the interpretability problem in machine learning approaches for survival analysis and also demonstrate how multi-omics data integration improves survival analysis and pathway enrichment. We use meta-learning, a machine-learning algorithm that is trained on a variety of related datasets and allows quick adaptations to new tasks, to perform survival analysis and pathway enrichment on pan-cancer datasets. In recent machine learning research, meta-learning has been effectively used for knowledge transfer among multiple related datasets.
resultsWe use meta-learning with Cox hazard loss to show that the integration of TCGA pan-cancer data increases the performance of survival analysis. We also apply advanced model interpretability method called DeepLIFT (Deep Learning Important FeaTures) to show different sets of enriched pathways for multi-omics and transcriptomics data. Our results show that multi-omics cancer survival analysis enhances performance compared with using transcriptomics or clinical data alone. Additionally, we show a correlation between variable importance assignment from DeepLIFT and gene coenrichment, suggesting that genes with higher and similar contribution scores are more likely to be enriched together in the same enrichment sets. AVAILABILITY AND IMPLEMENTATION: https://github.com/berkuva/TCGA-omics-integration.
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