ArticleBioinformatics (Oxford, England)2019
NEMO: cancer subtyping by integration of partial multi-omic data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers.
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116 citing papers in PubMed.
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- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- Assessing current capabilities for incorporating lipidomics in multiomics data integration.Briefings in bioinformatics · 2026Review
- Development and validation of clinical phenotypes in sepsis-associated liver injury using multi-algorithm consensus classification: a retrospective cohort study.BMC infectious diseases · 2026Article
- Multi-algorithm consensus classification identifies three distinct acute liver failure subtypes with differential treatment responses: a multi-database cohort study.Journal of advanced research · 2026Article
- A unified framework for correcting batch effects and integrating multi-omics data.Scientific reports · 2026Article
- AI based multiomics integration for cancer diagnosis and prognosis.Journal, genetic engineering & biotechnology · 2026Article
- SeOMLR: one-step multi-view latent representation with self-weighted ensemble learning for multi-omics cancer subtyping.Bioinformatics (Oxford, England) · 2026Article
- Remics: a redescription-based framework for multi-omics analysis.Frontiers in cell and developmental biology · 2026Article
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- PySimi: a unified framework for similarity measure evaluation in spectral clustering with applications to omics data.Frontiers in genetics · 2026Article
- Multi-omics integration in allergy, viral infections and asthma: a comprehensive analysis of bioinformatics tools.Frontiers in bioinformatics · 2026Article
56 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
motivationCancer subtypes were usually defined based on molecular characterization of single omic data. Increasingly, measurements of multiple omic profiles for the same cohort are available. Defining cancer subtypes using multi-omic data may improve our understanding of cancer, and suggest more precise treatment for patients.
resultsWe present NEMO (NEighborhood based Multi-Omics clustering), a novel algorithm for multi-omics clustering. Importantly, NEMO can be applied to partial datasets in which some patients have data for only a subset of the omics, without performing data imputation. In extensive testing on ten cancer datasets spanning 3168 patients, NEMO achieved results comparable to the best of nine state-of-the-art multi-omics clustering algorithms on full data and showed an improvement on partial data. On some of the partial data tests, PVC, a multi-view algorithm, performed better, but it is limited to two omics and to positive partial data. Finally, we demonstrate the advantage of NEMO in detailed analysis of partial data of AML patients. NEMO is fast and much simpler than existing multi-omics clustering algorithms, and avoids iterative optimization. AVAILABILITY AND IMPLEMENTATION: Code for NEMO and for reproducing all NEMO results in this paper is in github: https://github.com/Shamir-Lab/NEMO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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