Evidence map›Paper›PMID 37322419›Full record

ReviewBMC bioinformatics2023

Statistical methods and resources for biomarker discovery using metabolomics.

Najeha R Anwardeen, Ilhame Diboun, Younes Mokrab, Asma A Althani, Mohamed A Elrayess

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Najeha R AnwardeenResearch and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.
Ilhame DibounDepartment of Human Genetics, Sidra Medicine, Doha, Qatar.
Younes MokrabDepartment of Human Genetics, Sidra Medicine, Doha, Qatar.
Asma A AlthaniResearch and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.
Mohamed A ElrayessResearch and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar. m.elrayess@qu.edu.qa.ORCID http://orcid.org/0000-0003-3803-4604

Funding

Qatar National Research Fund NPRP13S-1230-190008
6 · The paper itself

Abstract

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Indexed as

Biomedical ResearchMetabolomicsBiomarkersData AnalysisHumansMetabolomeBiomarkersAnalytical workflowMetabolomicsMetabolomics toolsMultivariateStatistical methodsUnivariate

Identifiers

PMID37322419
PMCPMC10266963

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.