ArticleScientific reports2022
Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.Current oncology reports · 2026Pooled it
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Network analysis predicts pembrolizumab response in advanced NSCLC with PD-L1 < 50.Cancer cell international · 2026Article
- Advances in multi-omics research on neuroblastoma.Frontiers in pediatrics · 2026Review
- Deep Tumor Penetration Using Nanoparticle Delivery Systems: Programmed Design Strategies and Emerging Evaluation Platforms.International journal of nanomedicine · 2026Review
- AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.Clinical and experimental medicine · 2025Review
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues.Human genomics · 2025Review
- The human microbiome in clinical translation: from bench to bedside.Frontiers in microbiology · 2025Review
- Machine learning for precision diagnostics of autoimmunity.Scientific reports · 2024Article
- From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.Biomedicines · 2024Review
- Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine.Annual review of biomedical data science · 2024Review
- Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.Nature communications · 2024Article
- A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework.NPJ systems biology and applications · 2024Article
- A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.Heliyon · 2024Article
- Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review.Medical sciences (Basel, Switzerland) · 2024Review
- Multi-omics based artificial intelligence for cancer research.Advances in cancer research · 2024Review
- A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.Pediatric radiology · 2023Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the network features obtained from individual PSNs. We demonstrate our methods on two high-risk neuroblastoma datasets from SEQC project and TARGET project. We propose Deep Neural Network and Machine Learning methods with Recursive Feature Elimination as the predictor of survival status of neuroblastoma patients. Our results indicate that network-level fusion outperformed feature-level fusion for integration of different omics data whereas feature-level fusion is more suitable incorporating different feature types derived from same omics type. We conclude that the network-based methods are capable of handling heterogeneity and high dimensionality well in the integration of multi-omics.
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Registered trials
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.