Evidence map›Paper›PMID 42147321›Full record

ArticleFrontiers in pharmacology2026

Machine learning and metabolic modeling-based identification of hypoxia-driven metabolic signatures in pediatric cancers.

Subasree Sridhar, G K Suraishkumar

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 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

2 authors.

Subasree SridharDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building-1, Indian Institute of Technology Madras, Chennai, India.
G K SuraishkumarDepartment of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences Building-1, Indian Institute of Technology Madras, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic reprogramming in pediatric cancers under hypoxia has been much less studied compared to adult-onset cancers; such studies are essential to identify relevant therapeutic targets at different hypoxic levels. Genome-scale metabolic modeling studies of cancer metabolism are used to elucidate reprogrammed metabolic pathways, analyze heterogeneity between cancer types and subtypes, identify synthetic lethality, perform

Indexed as

genome-scale metabolic modelshypoxiamachine learningpediatric cancersreactive oxygen speciesreactive sulfur species

Identifiers

PMID42147321
PMCPMC13171815

What Socratic holds

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LicenceCC BY
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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 Socratic graph.