ArticleBiology direct2018
Multi-omics integration for neuroblastoma clinical endpoint prediction.
Article in Biology direct, 2018. 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.
- DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma.BioData mining · 2025Article
- A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework.NPJ systems biology and applications · 2024Article
- Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making.Journal of personalized medicine · 2023Review
- ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification.Nucleic acids research · 2023Article
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- Ten simple rules for organizing a special session at a scientific conference.PLoS computational biology · 2022Article
- Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator.Journal of diabetes science and technology · 2021Article
- Toxicokinetics of Arenobufagin and its Cardiotoxicity Mechanism Exploration Based on Lipidomics and Proteomics Approaches in Rats.Frontiers in pharmacology · 2021Article
- The ZNF750-RAC1 axis as potential prognostic factor for breast cancer.Cell death discovery · 2020Article
- Liquid biopsies and cancer omics.Cell death discovery · 2020Review
- Commensal microbes and p53 in cancer progression.Biology direct · 2020Review
- Pathophysiology of Crohn's disease inflammation and recurrence.Biology direct · 2020Review
- Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data.Scientific reports · 2020Article
- Predictability of drug-induced liver injury by machine learning.Biology direct · 2020Article
- Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.Frontiers in oncology · 2020Review
- Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.Frontiers in oncology · 2020Review
- A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.BMC medical genomics · 2019Article
- A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models.Biology direct · 2019Article
- The application of artificial neural networks in metabolomics: a historical perspective.Metabolomics : Official journal of the Metabolomic Society · 2019Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-throughput methodologies such as microarrays and next-generation sequencing are routinely used in cancer research, generating complex data at different omics layers. The effective integration of omics data could provide a broader insight into the mechanisms of cancer biology, helping researchers and clinicians to develop personalized therapies.
resultsIn the context of CAMDA 2017 Neuroblastoma Data Integration challenge, we explore the use of Integrative Network Fusion (INF), a bioinformatics framework combining a similarity network fusion with machine learning for the integration of multiple omics data. We apply the INF framework for the prediction of neuroblastoma patient outcome, integrating RNA-Seq, microarray and array comparative genomic hybridization data. We additionally explore the use of autoencoders as a method to integrate microarray expression and copy number data.
conclusionsThe INF method is effective for the integration of multiple data sources providing compact feature signatures for patient classification with performances comparable to other methods. Latent space representation of the integrated data provided by the autoencoder approach gives promising results, both by improving classification on survival endpoints and by providing means to discover two groups of patients characterized by distinct overall survival (OS) curves. REVIEWERS: This article was reviewed by Djork-Arné Clevert and Tieliu Shi.
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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.