ReviewNucleic acids research2018
Multi-omic and multi-view clustering algorithms: review and cancer benchmark.
Review in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 240 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
240 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition.Science advances · 2022Trial
- Ensemble clustering: A practical tutorial.Behavior research methods · 2026Article
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- One-Dimensional Molecular Coordinate Orders Amino Acids across Water-Mediated Processes.Journal of chemical information and modeling · 2026Article
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- CAECC-Subtyper: A Novel Convolutional Autoencoder Framework for Integrating Multi-omics Data in Cancer Subtyping.Biochemical genetics · 2026Article
- Hierarchical Multi-Omics Trajectory Prediction for fecal microbiota transplantation: a novel machine learning framework for small-sample longitudinal multi-omics integration.Briefings in bioinformatics · 2026Article
- UDEC-MO: an uncertainty-guided deep embedded clustering framework for bulk and single-cell multi-omics data.Briefings in bioinformatics · 2026Article
- Multivariate Random Forests for Cross-Modal Multi-Omics Integration.bioRxiv : the preprint server for biology · 2026Article
- Diet-Microbiota-Immune Interactions in Hepatocellular Carcinoma: An Immunometabolic and Spatial Perspective.Nutrients · 2026Review
- Clustering longitudinal multivariate trajectories using an ensemble of principal component trees.BMC medical research methodology · 2026Article
- Praxis-BGM: clustering of omics data using semi-supervised transfer learning for Gaussian mixture models via natural-gradient variational inference.Bioinformatics (Oxford, England) · 2026Article
- Analysis and visualization of expression patterns with fuzzy sets as FlowSets.NAR genomics and bioinformatics · 2026Article
- Multi-omics insights into uric acid metabolism in cardiometabolic disease: from genetics to metabolomics.Biomedical journal · 2026Review
- Article
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- CEDR: robust consensus cancer subtyping with multi-omics data via ensemble dimensionality reduction.Briefings in bioinformatics · 2026Article
- Article
180 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent high throughput experimental methods have been used to collect large biomedical omics datasets. Clustering of single omic datasets has proven invaluable for biological and medical research. The decreasing cost and development of additional high throughput methods now enable measurement of multi-omic data. Clustering multi-omic data has the potential to reveal further systems-level insights, but raises computational and biological challenges. Here, we review algorithms for multi-omics clustering, and discuss key issues in applying these algorithms. Our review covers methods developed specifically for omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types. In addition, using cancer data from TCGA, we perform an extensive benchmark spanning ten different cancer types, providing the first systematic comparison of leading multi-omics and multi-view clustering algorithms. The results highlight key issues regarding the use of single- versus multi-omics, the choice of clustering strategy, the power of generic multi-view methods and the use of approximated p-values for gauging solution quality. Due to the growing use of multi-omics data, we expect these issues to be important for future progress in the field.
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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.