ReviewTrends in genetics : TIG2018
Enter the Matrix: Factorization Uncovers Knowledge from Omics.
Review in Trends in genetics : TIG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
136 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Single-cell transcriptome landscape of circulating CD4Cell genomics · 2024Pooled it
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
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- Differential assembly of mouse and human tumor microenvironments.Nature immunology · 2026Article
- Local gene editing of fibroblasts in tumors reveals a new cancer-associated fibroblast state.The Journal of experimental medicine · 2026Article
- Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.Nature genetics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Tumor Heterogeneity Induces Pro- and Anti-metastatic Myeloid Cell Phenotypes in Breast Cancer Lung Metastasis.bioRxiv : the preprint server for biology · 2026Article
- Identification and clustering analysis of drug-responsive temporally varying genes through high-frequency longitudinal RNA sequencing.Scientific reports · 2026Article
- A cell fate specification and transition mechanism for human foveolar cone subtype patterning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Multiomic integration reveals tumoral heterogeneity of lipid dependence within lethal group 3 medulloblastoma.Cancer cell · 2026Article
- UCell and pyUCell: single-cell gene signature scoring for R and Python.Bioinformatics (Oxford, England) · 2026Article
- Structure of theMicrobiology spectrum · 2026Article
- Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data.Biostatistics (Oxford, England) · 2026Article
- Genetic and embryonic transcriptome analyses reveal the molecular and developmental basis of Mayer-Rokitansky-Küster-Hauser syndrome.Journal of medical genetics · 2026Article
- Review
- Statistical design of a synthetic microbiome that suppresses diverse gut pathogens.bioRxiv : the preprint server for biology · 2025Article
- A Roadmap for the Future of Systems Biology in Cancer Research.Cancer research · 2025Review
76 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
12 authors.
Funding
Abstract
Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.
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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.