ReviewImmunology2018
Using 'collective omics data' for biomedical research training.
Review in Immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed.
- CSF1 regulates inflammation and apoptosis in intervertebral disc degeneration.Scientific reports · 2026Article
- Abundance ofFrontiers in immunology · 2023Article
- Assessing the potential relevance of CEACAM6 as a blood transcriptional biomarker.F1000Research · 2022Article
- Connection of BANK1, Tolerance, Regulatory B cells, and Apoptosis: Perspectives of a Reductionist Investigation.Frontiers in immunology · 2021Article
- Organizing training workshops on gene literature retrieval, profiling, and visualization for early career researchers.F1000Research · 2021Article
- Annexin A3 in sepsis: novel perspectives from an exploration of public transcriptome data.Immunology · 2020Review
- A curated collection of transcriptome datasets to investigate the molecular mechanisms of immunoglobulin E-mediated atopic diseases.Database : the journal of biological databases and curation · 2019Article
- Long-Chain Acyl-CoA Synthetase 1 Role in Sepsis and Immunity: Perspectives From a Parallel Review of Public Transcriptome Datasets and of the Literature.Frontiers in immunology · 2019Review
- Bioinformatics for immunologists.Immunology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systems-scale molecular profiling data accumulating in public repositories may constitute a useful resource for immunologists. It is for instance likely that information relevant to their chosen line of research be found among the more than 90,000 data series available in the NCBI Gene Expression Omnibus. Such 'collective omics data' may also be employed as source material for training purposes. This is the case when training curricula aim at the development of bioinformatics skills necessary for the analysis, interpretation or visualization of data generated on global scales. But 'collective omics data' may also be reused for training purposes to foster the development of the skills and 'mental habits' underpinning traditional reductionist science approaches. This review describes a small-scale initiative involving investigators, for the most part immunologists, having engaged in a range of training activities relying on 'collective omics data'.
Indexed as
Identifiers
What OpenQuestion holds
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.