ArticleFrontiers in genetics2018
Deep Learning-Based Multi-Omics Data Integration Reveals Two Prognostic Subtypes in High-Risk Neuroblastoma.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.
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Who cites it
85 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Quantum mechanics-based multitensor AI/ML uniquely able to discover, validate, and interpret predictors from small-cohort noisy high-dimensional multiomic data.APL quantum · 2026Article
- Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.Biochemistry and biophysics reports · 2026Article
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
- MOTLAB: A Weighted Multi-Omics Transfer Learning Approach to Mitigate Breast Cancer Racial Disparities.bioRxiv : the preprint server for biology · 2025Article
- Review
- Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Review
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma.BioData mining · 2025Article
- Risk stratification in neuroblastoma patients through machine learning in the multicenter PRIMAGE cohort.Frontiers in oncology · 2025Article
- Deep learning-based approaches for multi-omics data integration and analysis.BioData mining · 2024Review
- Identification and validation of a novel five-gene signature in high-risk MYCN-not-amplified neuroblastoma.Discover oncology · 2024Article
- Identification of cancer risk groups through multi-omics integration using autoencoder and tensor analysis.Scientific reports · 2024Article
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.PLoS computational biology · 2024Article
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.bioRxiv : the preprint server for biology · 2024Article
- Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review.Medical sciences (Basel, Switzerland) · 2024Review
- Article
- DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.BioData mining · 2023Article
- Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making.Journal of personalized medicine · 2023Review
- A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma.Journal of cancer research and clinical oncology · 2023Article
25 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.