ArticleNucleic acids research2012
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
170 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
- A powerful representation learning method for enhanced analysis of incomplete multi-omics data.NPJ systems biology and applications · 2026Article
- Review
- LONMF: a non-negative matrix factorization model based on graph Laplacian and optimal transmission for paired single-cell multi-omics data integration.BMC bioinformatics · 2025Article
- An Improved Deep Semi-supervised JNMF Method for Biomarker Extraction of Alzheimer's Disease.Journal of molecular neuroscience : MN · 2025Article
- JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data.bioRxiv : the preprint server for biology · 2025Article
- Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease.Briefings in bioinformatics · 2025Review
- Autoencoders with shared and specific embeddings for multi-omics data integration.BMC bioinformatics · 2025Article
- LSGI: interpretable spatial gradient analysis for spatial transcriptomics data.Genome biology · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.Bioinformatics (Oxford, England) · 2025Article
- sTPLS: identifying common and specific correlated patterns under multiple biological conditions.Briefings in bioinformatics · 2025Article
- NMFProfiler: a multi-omics integration method for samples stratified in groups.Bioinformatics (Oxford, England) · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Multi-tissue metabolomics analysis reveals susceptible factors for chemotherapy-induced hepatotoxicity in colorectal cancer patients.Frontiers in pharmacology · 2025Article
- Decoding sex differences in human immunity through systems immunology.Oxford open immunology · 2025Review
- MoNETA: MultiOmics Network Embedding for SubType Analysis.NAR genomics and bioinformatics · 2024Article
- Integrating multi-omics data of childhood asthma using a deep association model.Fundamental research · 2024Article
110 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called 'multi-dimensional genomic data'. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional 'omic' data.
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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.