ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2025
Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Uncovering core regulators of multi-abiotic stress adaptation in Arabidopsis thaliana through integrative meta-analysis and machine learning with RT-qPCR validation.Scientific reports · 2026Pooled it
- Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.BMC genomics · 2026Review
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- Initial Physiological and Molecular Adjustments Underpin Salinity Tolerance During Wheat Germination and Early Seedling Development.Plants (Basel, Switzerland) · 2026Article
- Integration of LLMs and VLMs in plant stress phenotyping: From trait recognition to decision support.Plant phenomics (Washington, D.C.) · 2026Review
- AI and Biotechnology to Combat Aflatoxins: Future Directions for Modern Technologies in Reducing Aflatoxin Risk.Toxins · 2025Review
- Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Abiotic stresses such as drought, heat, cold, salinity and flooding significantly impact plant growth, development and productivity. As the planet has warmed, these abiotic stresses have increased in frequency and intensity, affecting the global food supply and making it imperative to develop stress-resilient crops. In the past 20 years, the development of omics technologies has contributed to the growth of datasets for plants grown under a wide range of abiotic environments. Integration of these rapidly growing data using machine-learning (ML) approaches can complement existing breeding efforts by providing insights into the mechanisms underlying plant responses to stressful conditions, which can be used to guide the design of resilient crops. In this review, we introduce ML approaches and provide examples of how researchers use these approaches to predict molecular activities, gene functions and genotype responses under stressful conditions. Finally, we consider the potential and challenges of using such approaches to enable the design of crops that are better suited to a changing environment.This article is part of the theme issue 'Crops under stress: can we mitigate the impacts of climate change on agriculture and launch the 'Resilience Revolution'?'.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.