Evidence map›Paper›PMID 42422472›Full record

ArticleBioinformatics advances2026

stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.

Julian Welling, Miriam Balzer, Leah Consten, Stefan Bletz, Jan Buer, Valerie Chapot, Dag Harmsen, Evelyn Heintschel von Heinegg, Alexander Mellmann, Wolfgang Pölking and 5 more

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Article in Bioinformatics advances, 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

15 authors.

Julian WellingDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.ORCID https://orcid.org/0000-0001-8197-076X
Miriam BalzerDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.ORCID https://orcid.org/0009-0004-2083-5651
Leah ConstenDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.ORCID https://orcid.org/0009-0006-6657-8018
Stefan BletzInstitute of Hygiene, University Hospital Münster, Münster, 48149, Germany.ORCID https://orcid.org/0009-0006-8826-3439
Jan BuerDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.ORCID https://orcid.org/0000-0002-7602-1698
Valerie ChapotDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.
Dag HarmsenRidom GmbH, Münster, 48149, Germany.
Evelyn Heintschel von HeineggDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.
Alexander MellmannInstitute of Hygiene, University Hospital Münster, Münster, 48149, Germany.ORCID https://orcid.org/0000-0002-0649-5185
Wolfgang PölkingInstitute of Hygiene, University Hospital Münster, Münster, 48149, Germany.ORCID https://orcid.org/0009-0005-0887-9317
Friederike SalhöferDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.
Frieder SchaumburgInstitute of Medical Microbiology, University Hospital Münster, Münster, 48149, Germany.ORCID https://orcid.org/0000-0002-9168-9290
Natalie ScherffInstitute of Hygiene, University Hospital Münster, Münster, 48149, Germany.ORCID https://orcid.org/0000-0002-8414-0259
Niklas WiesmannInstitute of Medical Microbiology, University Hospital Münster, Münster, 48149, Germany.ORCID https://orcid.org/0009-0007-7238-2852
Folker MeyerDepartment of Medicine, University of Duisburg-Essen, Essen, 45147, Germany.ORCID https://orcid.org/0000-0003-1112-2284

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Antimicrobial resistance is a growing global threat, creating a need for rapid and accurate antimicrobial susceptibility testing. Current phenotypic antimicrobial susceptibility testing methods rely on prior isolation and cultivation, making them time-consuming. Whole genome sequencing combined with machine learning offers a faster and cost-effective alternative, but existing approaches are often limited in species coverage, antimicrobial scope, or data availability. Results: We developed stackPredAMR, a machine learning framework for predicting resistance to 18 antimicrobial agents in three clinically important bacterial species: Availability and implementation: Source code and datasets (database-driven reference approach, sample lists, and input features) are available at WIN-KID repository (https://github.com/IKIM-Essen/WIN-KID/tree/v1.0.0.0) and the release page (https://github.com/IKIM-Essen/WIN-KID/releases/tag/v1.0.0.0).

Identifiers

PMID42422472
PMCPMC13342711

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Registered trials

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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.