ReviewBriefings in bioinformatics2016
Dimension reduction techniques for the integrative analysis of multi-omics data.
Review in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers.
What it found
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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.
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.
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Who cites it
173 citing papers in PubMed.
- Trial
- Diagnosing Anaerobic Digesters' Function and Performance: From Meta-Omics to Integrated Meta-Omics Analyses.Environmental microbiology reports · 2026Review
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- From case-control averages to validated subgroups: Interpreting inflammatory and neuroaxonal-injury biomarkers in schizophrenia and major depression.Brain, behavior, & immunity - health · 2026Review
- VDJ-REMIX: REpertoire Module Identification and eXploration.Bioinformatics (Oxford, England) · 2026Article
- Deep survival analysis in multimodal medical data: a parametric and probabilistic approach with competing risks.Scientific reports · 2026Article
- Multiomic integration reveals tumoral heterogeneity of lipid dependence within lethal group 3 medulloblastoma.Cancer cell · 2026Article
- Integrative bioinformatics approaches for early detection biomarkers in ovarian cancer.Annals of medicine and surgery (2012) · 2026Review
- Multilayer metabolomic integration reveals bioenergetic disruption in Long COVID.Journal of translational medicine · 2026Article
- Integrative multi-omics and network biology in cardiovascular disease: a systems-level framework for translational discovery.Frontiers in systems biology · 2026Review
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- Integrated multi-omics and deep learning analysis reveals neurotransmitter metabolism regulatory mechanisms of Tianwang Buxin Dan.Frontiers in neuroscience · 2026Article
- The impact of epigenetics on tumor metabolism: Friend or foe in drug response?Frontiers in epigenetics and epigenomics · 2026Review
- Refining treatment strategies for non-small cell lung cancer lacking actionable mutations: insights from multi-omics studies.British journal of cancer · 2025Review
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Integrating Machine Learning and Multi-Omics to Explore Neutrophil Heterogeneity.Biomedicines · 2025Review
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Review
113 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
6 authors.
Funding
Abstract
State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.
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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.