ArticlePlant physiology2014
Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice.
Article in Plant physiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 10 of them syntheses 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
64 citing papers in PubMed, 10 syntheses or guidelines pooled it.
- Comparative identification of abiotic stress-responsive differentially expressed genes in chickpea using unsupervised machine learning, and traditional meta-analysis.Genes & genomics · 2026Pooled 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
- Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis.Nature communications · 2025Pooled it
- Transcriptome meta-analysis of abiotic stresses-responsive genes and identification of candidate transcription factors for broad stress tolerance in wheat.Protoplasma · 2023Pooled it
- Identification of Key Gene Network Modules and Hub Genes Associated with Wheat Response to Biotic Stress Using Combined Microarray Meta-analysis and WGCN Analysis.Molecular biotechnology · 2023Pooled it
- Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species.Scientific reports · 2023Pooled it
- Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis.Functional & integrative genomics · 2020Pooled it
- Identification of drought stress-responsive genes in rice (Journal of genetics · 2020Pooled it
- Comprehensive meta-analysis, co-expression, and miRNA nested network analysis identifies gene candidates in citrus against Huanglongbing disease.BMC plant biology · 2015Pooled it
- Identification of conserved drought-adaptive genes using a cross-species meta-analysis approach.BMC plant biology · 2015Pooled it
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Article
- Article
- 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
- Decoding the ZmNF-YC1-ZmAPRG pathway for phosphorus efficiency.Frontiers in plant science · 2025Article
- Current status of molecular rice breeding for durable and broad-spectrum resistance to major diseases and insect pests.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Review
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- Predicting rice diseases using advanced technologies at different scales: present status and future perspectives.aBIOTECH · 2023Review
- Soybean gene co-expression network analysis identifies two co-regulated gene modules associated with nodule formation and development.Molecular plant pathology · 2023Article
- Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abiotic and biotic stress responses are traditionally thought to be regulated by discrete signaling mechanisms. Recent experimental evidence revealed a more complex picture where these mechanisms are highly entangled and can have synergistic and antagonistic effects on each other. In this study, we identified shared stress-responsive genes between abiotic and biotic stresses in rice (Oryza sativa) by performing meta-analyses of microarray studies. About 70% of the 1,377 common differentially expressed genes showed conserved expression status, and the majority of the rest were down-regulated in abiotic stresses and up-regulated in biotic stresses. Using dimension reduction techniques, principal component analysis, and partial least squares discriminant analysis, we were able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of differentially expressed genes. Furthermore, using a random forests decision tree model, eight out of 10 stress conditions were classified with high accuracy. Comparison of genes contributing most to the accurate classification by partial least squares discriminant analysis, recursive-support vector machine, and random forests revealed 196 common genes with a dynamic range of expression levels in multiple stresses. Functional enrichment and coexpression network analysis revealed the different roles of transcription factors and genes responding to phytohormones or modulating hormone levels in the regulation of stress responses. We envisage the top-ranked genes identified in this study, which highly discriminate abiotic and biotic stresses, as key components to further our understanding of the inherently complex nature of multiple stress responses in plants.
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.