ReviewBMC systems biology2014
Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data.
Review in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression.Orphanet journal of rare diseases · 2025Article
- AutoFocus: a hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction.Communications biology · 2024Article
- A gene network-driven approach to infer novel pathogenicity-associated genes: application tomSystems · 2023Article
- Guide to Metabolomics Analysis: A Bioinformatics Workflow.Metabolites · 2022Review
- ECO-CollecTF: A Corpus of Annotated Evidence-Based Assertions in Biomedical Manuscripts.Frontiers in research metrics and analytics · 2021Article
- Data Integration Challenges for Machine Learning in Precision Medicine.Frontiers in medicine · 2021Review
- STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse.Scientific data · 2019Article
- Distinct skeletal muscle molecular responses to pulmonary rehabilitation in chronic obstructive pulmonary disease: a cluster analysis.Journal of cachexia, sarcopenia and muscle · 2019Article
- The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study.American journal of obstetrics and gynecology · 2017Article
- From big data analysis to personalized medicine for all: challenges and opportunities.BMC medical genomics · 2015Review
- GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.PloS one · 2015Article
- Data integration in the era of omics: current and future challenges.BMC systems biology · 2014Review
- Gene network biological validity based on gene-gene interaction relevance.TheScientificWorldJournal · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-throughput omics technologies have enabled the measurement of many genes or metabolites simultaneously. The resulting high dimensional experimental data poses significant challenges to transcriptomics and metabolomics data analysis methods, which may lead to spurious instead of biologically relevant results. One strategy to improve the results is the incorporation of prior biological knowledge in the analysis. This strategy is used to reduce the solution space and/or to focus the analysis on biological meaningful regions. In this article, we review a selection of these methods used in transcriptomics and metabolomics. We combine the reviewed methods in three groups based on the underlying mathematical model: exploratory methods, supervised methods and estimation of the covariance matrix. We discuss which prior knowledge has been used, how it is incorporated and how it modifies the mathematical properties of the underlying methods.
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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.