SynthesisScientific reports2023
Drought-responsive genes in tomato: meta-analysis of gene expression using machine learning.
Synthesis in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Meta-analysis, WGCNA, and machine learning converge on a four-gene biomarker panel for heat stress tolerance in Solanum lycopersicum.Scientific reports · 2026Pooled it
- Drought Stress Mediated Changes in Food Crops: Mechanisms and Remediation Strategies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- The HD-Zip II Transcription FactorAntioxidants (Basel, Switzerland) · 2026Article
- Field pathogenomics and evolutionary conservation unveil CRISPR-targetable susceptibility genes for wheat blast resistance.Scientific reports · 2026Article
- Post-transcriptional regulation of light-stress responses and predictive modeling in vegetableFrontiers in plant science · 2026Review
- Cold-Tolerant Bacteria Isolated from Alpine Plants Can Promote Growth and Mitigate Cold Stress in Tomato Seedlings by Complex Transcriptional Reprogramming of Stress-Related Genes.Plants (Basel, Switzerland) · 2025Article
- Dissection of major QTLs and candidate genes for seedling stage salt/drought tolerance in tomato.BMC genomics · 2024Article
- Exploring the Genetic Basis of Drought Tolerance inInternational journal of molecular sciences · 2024Article
- Endophytic Colonization ofInternational journal of molecular sciences · 2024Article
- A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data.Plants (Basel, Switzerland) · 2024Article
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plants have diverse molecular mechanisms to protect themselves from biotic and abiotic stressors and adapt to changing environments. To uncover the genetic potential of plants, it is crucial to understand how they adapt to adverse conditions by analyzing their genomic data. We analyzed RNA-Seq data from different tomato genotypes, tissue types, and drought durations. We used a time series scale to identify early and late drought-responsive gene modules and applied a machine learning method to identify the best responsive genes to drought. We demonstrated six candidate genes of tomato viz. Fasciclin-like arabinogalactan protein 2 (FLA2), Amino acid transporter family protein (ASCT), Arginine decarboxylase 1 (ADC1), Protein NRT1/PTR family 7.3 (NPF7.3), BAG family molecular chaperone regulator 5 (BAG5) and Dicer-like 2b (DCL2b) were responsive to drought. We constructed gene association networks to identify their potential interactors and found them drought-responsive. The identified candidate genes can help to explore the adaptation of tomato plants to drought. Furthermore, these candidate genes can have far-reaching implications for molecular breeding and genome editing in tomatoes, providing insights into the molecular mechanisms that underlie drought adaptation. This research underscores the importance of the genetic basis of plant adaptation, particularly in changing climates and growing populations.
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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.