Evidence map›Paper›PMID 40552708›Full record

ReviewJournal of integrative plant biology2025

Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.

Judit Dobránszki, Valya Vassileva, Dolores R Agius, Panagiotis Nikolaou Moschou, Philippe Gallusci, Margot M J Berger, Dóra Farkas, Marcos Fernando Basso, Federico Martinelli

Abstract readReview
In one paragraph

Review in Journal of integrative plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

9 authors.

Judit DobránszkiCentre for Agricultural Genomics and Biotechnology, University of Debrecen, PO Box 12., Nyíregyháza 4400, Hungary.ORCID https://orcid.org/0000-0001-7624-6398
Valya VassilevaDepartment of Molecular Biology and Genetics, Institute of Plant Physiology and Genetics, Bulgarian Academy of Sciences, Sofia 1113, Bulgaria.ORCID https://orcid.org/0000-0002-9055-8002
Dolores R AgiusDepartment of Biology, Ġ.F. Abela Junior College, Ġuzè Debono Square, Msida MSD 1252, Malta.ORCID https://orcid.org/0000-0003-1819-2883
Panagiotis Nikolaou MoschouDepartment of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, Sweden.ORCID https://orcid.org/0000-0001-7212-0595
Philippe GallusciUMR Ecophysiologie et Génomique Fonctionnelle de la Vigne (EGFV), University of Bordeaux, Bordeaux Sciences Agro, Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Institut des Sciences de la Vigne et du Vin (ISVV), Villenave d'Ornon, 33882, France.ORCID https://orcid.org/0000-0003-1163-8299
Margot M J BergerUMR Ecophysiologie et Génomique Fonctionnelle de la Vigne (EGFV), University of Bordeaux, Bordeaux Sciences Agro, Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Institut des Sciences de la Vigne et du Vin (ISVV), Villenave d'Ornon, 33882, France.ORCID https://orcid.org/0000-0002-4435-3401
Dóra FarkasCentre for Agricultural Genomics and Biotechnology, University of Debrecen, PO Box 12., Nyíregyháza 4400, Hungary.ORCID https://orcid.org/0009-0006-6101-6104
Marcos Fernando BassoDepartment of Biology, University of Florence, Firenze, 50019, Italy.ORCID https://orcid.org/0000-0001-8192-8959
Federico MartinelliDepartment of Biology, University of Florence, Firenze, 50019, Italy.

Funding

Nemzeti Kutatási, Fejlesztési és Innovaciós Alap TKP2021-EGA-20
6 · The paper itself

Abstract

Plants exhibit remarkable abilities to learn, communicate, memorize, and develop stimulus-dependent decision-making circuits. Unlike animals, plant memory is uniquely rooted in cellular, molecular, and biochemical networks, lacking specialized organs for these functions. Consequently, plants can effectively learn and respond to diverse challenges, becoming used to recurring signals. Artificial intelligence (AI) and machine learning (ML) represent the new frontiers of biological sciences, offering the potential to predict crop behavior under environmental stresses associated with climate change. Epigenetic mechanisms, serving as the foundational blueprints of plant memory, are crucial in regulating plant adaptation to environmental stimuli. They achieve this adaptation by modulating chromatin structure and accessibility, which contribute to gene expression regulation and allow plants to adapt dynamically to changing environmental conditions. In this review, we describe novel methods and approaches in AI and ML to elucidate how plant memory occurs in response to environmental stimuli and priming mechanisms. Furthermore, we explore innovative strategies exploiting transgenerational memory for plant breeding to develop crops resilient to multiple stresses. In this context, AI and ML can aid in integrating and analyzing epigenetic data of plant stress responses to optimize the training of the parental plants.

Indexed as

Artificial IntelligenceEpigenesis, GeneticPlantsEpigenomicsMachine Learningdeep learningDNA methylationgene expressionmachine learningstress memorytransgenerational inheritance

Identifiers

PMID40552708
PMCPMC12402757

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.