Evidence map›Paper›PMID 40243585›Full record

ArticleInternational journal of molecular sciences2025

Cell Wall-Based Machine Learning Models to Predict Plant Growth Using Onion Epidermis.

Celia Khoulali, Juan Manuel Pastor, Javier Galeano, Kris Vissenberg, Eva Miedes

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. 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

5 authors.

Celia KhoulaliDepartment of Biotechnology-Plant Biology, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0002-6468-6799
Juan Manuel PastorComplex System Research Group-UPM, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0002-1067-3642
Javier GaleanoComplex System Research Group-UPM, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0003-0706-4317
Kris VissenbergDepartment of Biology, Faculty of Science, University of Antwerp, 2020 Antwerpen, Belgium.ORCID 0000-0003-0292-2095
Eva MiedesDepartment of Biotechnology-Plant Biology, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain.ORCID 0000-0003-2899-1494

Funding

Algerian government 057Bis/PG/Espagne/2020-2021Comunidad de Madrid Convenio Plurianual con la Universidad Politécnica de Madrid en la línea de actuación Programa de Excelencia para el Profesorado Universitario.Ministerio de Ciencia, Innovación y Universidades ChaSisCOMA (PID2021-122711NB-C21)Universidad Politécnica de Madrid VAGI23JPMCUniversidad Politécnica de Madrid VAGI24JPMCUniversity of Antwerp and the Research Foundation Flanders FWO, grant G013023N
6 · The paper itself

Abstract

The plant cell wall (CW) is a physical barrier that plays a dual role in plant physiology, providing structural support for growth and development. Understanding the dynamics of CW growth is crucial for optimizing crop yields. In this study, we employed onion (

Indexed as

Cell WallMachine LearningOnionsPlant EpidermisCelluloseGene Expression Regulation, PlantSpectroscopy, Fourier Transform InfraredSupport Vector MachineCelluloseAllium cepa L.cell wall compositioncell wall enzymesmachine learningmodelingonion epidermisplant growth

Identifiers

PMID40243585
PMCPMC11989001

What OpenQuestion holds

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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 OpenQuestion graph.