ArticlePloS one2017
Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers, 2 of them syntheses that pooled it.
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Who cites it
131 citing papers in PubMed, 2 syntheses or guidelines pooled it, 255 citations in OpenAlex.
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
- Continuity of transcriptomes among colorectal cancer subtypes based on meta-analysis.Genome biology · 2018Pooled it
- Gut microbiome modulates the outcome in primary central nervous system lymphoma patients undergoing chemotherapy: An ancillary study from the BLOCAGE trial.Neuro-oncology · 2025Trial
- Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data.NAR genomics and bioinformatics · 2026Article
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Integrative transcriptomic and experimental analysis identifies GREM1 as a pro-metastatic mediator in lung adenocarcinoma.Journal of translational medicine · 2026Article
- Multivariate Random Forests for Cross-Modal Multi-Omics Integration.bioRxiv : the preprint server for biology · 2026Article
- Multi-phase hybrid metabolomics framework identifies clinically applicable plasma signatures for early detection of gastric cancer.Nature communications · 2026Article
- 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
- SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data.Genome biology · 2026Article
- Review
- 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
- Spatial transcriptomics reveals tumor microenvironment-driven subtypes of invasive lobular carcinoma.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- iModMix: integrative module analysis for multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- Article
- United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes.iScience · 2026Article
- Multimodal-based analysis of single-cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations.Genome medicine · 2026Article
- CDCSI: a machine learning-based interpretable cell death and cellular senescence index for prognosis improvement, immune landscape characterization, and therapeutic response prediction in head and neck squamous cell carcinoma.Frontiers in immunology · 2026Article
71 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Integrative analyses of high-throughput 'omic data, such as DNA methylation, DNA copy number alteration, mRNA and protein expression levels, have created unprecedented opportunities to understand the molecular basis of human disease. In particular, integrative analyses have been the cornerstone in the study of cancer to determine molecular subtypes within a given cancer. As malignant tumors with similar morphological characteristics have been shown to exhibit entirely different molecular profiles, there has been significant interest in using multiple 'omic data for the identification of novel molecular subtypes of disease, which could impact treatment decisions. Therefore, we have developed intNMF, an integrative approach for disease subtype classification based on non-negative matrix factorization. The proposed approach carries out integrative clustering of multiple high dimensional molecular data in a single comprehensive analysis utilizing the information across multiple biological levels assessed on the same individual. As intNMF does not assume any distributional form for the data, it has obvious advantages over other model based clustering methods which require specific distributional assumptions. Application of intNMF is illustrated using both simulated and real data from The Cancer Genome Atlas (TCGA).
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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.