ReviewFrontiers in oncology2020
Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.
Review in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 122 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Multi-level Molecular Profiling of High Acute Stress
A Pilot-study on Multi-level Molecular Profiling of Peak Performance in Endurance Sports
Who cites it
122 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- Is radiomic MRI a feasible alternative to OncotypeDX® recurrence score testing? A systematic review and meta-analysis.BJS open · 2021Pooled it
- Article
- CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.Translational andrology and urology · 2026Article
- The emerging roles of alternative splicing in modulating tumor immune responses and immunotherapies.Cell death and differentiation · 2026Review
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- Oncogenic cell fate decision in breast epithelial cells I: growth factors and mechanisms.Research square · 2026Article
- Assessing current capabilities for incorporating lipidomics in multiomics data integration.Briefings in bioinformatics · 2026Review
- SCONE: a subset-contrastive method for multi-omics network embedding.Briefings in bioinformatics · 2026Article
- Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models.PLoS computational biology · 2026Article
- IRDRM: Exploring Biological Traits and Clinical Significance in Breast Cancer Through Gene Pairs and Somatic Mutation Profiles.Interdisciplinary sciences, computational life sciences · 2026Article
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- From Data to Decision: Integrating Bioinformatics into Glioma Patient Stratification and Immunotherapy Selection.International journal of molecular sciences · 2026Review
- Multi-omics integration in allergy, viral infections and asthma: a comprehensive analysis of bioinformatics tools.Frontiers in bioinformatics · 2026Article
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- The Role of Exosomal Biomarkers in the Monitoring of Bioactivation and Metabolite Clearance of Chemotherapeutics in Cancer Patients.Current drug metabolism · 2026Review
- Article
- Precision medicine in rheumatoid arthritis: advances and clinical applications of multi-omics biomarkers.Frontiers in immunology · 2026Review
- Emerging small molecule strategies target cancer stem cells through ferroptosis and metabostemness.Discover oncology · 2025Review
- Article
62 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.
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What OpenQuestion holds
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.