Evidence map›Paper›PMID 42506421›Full record

ArticleMetabolites2026

EHMN2026

Igor Goryanin, Leonid Slovianov, Irina V Goryanin, Alexander Kel

Abstract read
In one paragraph

Article in Metabolites, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Igor GoryaninSchool of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.ORCID 0000-0002-8293-774X
Leonid SlovianovIQANOVA Ltd., Edinburgh EH10 5LZ, UK.
Irina V GoryaninIQANOVA Ltd., Edinburgh EH10 5LZ, UK.
Alexander KelgeneXplain GmbH, 38300 Wolfenbüttel, Germany.ORCID 0000-0001-6775-2467

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesDiagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechanistic interpretation requires linking measured metabolites to enzymes, pathways, regulatory context, disease knowledge and, increasingly, AI-assisted quantitative systems pharmacology (AI-QSP) workflows. We developed EHMN2026

methodsEHMN2026

resultsPromoter analysis of 1681 EHMN2026

conclusionsEHMN2026

Indexed as

AI-QSPbiomarkersdiagnostic metabolitesEHMN2026®geneXplaingenome-scale metabolic modelHumanPSD™inborn errors of metabolismmetabolomicsoncometabolitesquantitative systems pharmacologySBMLtranscriptional regulatory potentialTRANSFAC®TRANSPATH®

Identifiers

PMID42506421
PMCPMC13413855

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.