ReviewBiology2023
An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture.
Review in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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
39 citing papers in PubMed.
- Morphological and Pomological Characterization of Indigenous Pear Genotypes (Food science & nutrition · 2026Article
- AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology.Plants (Basel, Switzerland) · 2026Review
- Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.Functional & integrative genomics · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Phytohormones as key regulators of plant resilience under salinity and extreme temperatures.Planta · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Review
- The Floral Bottleneck in a Changing Climate: Molecular Mechanisms, Knowledge Gaps, and Future Directions.International journal of molecular sciences · 2026Review
- Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for next-generation crop design.Plant communications · 2026Review
- Review
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.Plant phenomics (Washington, D.C.) · 2026Article
- The emerging impact of CRISPR and gene editing on global crop improvement.Transgenic research · 2026Review
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Liquid Biopsy and Multi-Omic Biomarkers in Breast Cancer: Innovations in Early Detection, Therapy Guidance, and Disease Monitoring.Biomedicines · 2025Review
- Review
- Plant-pathogen interactions: making the case for multi-omics analysis of complex pathosystems.Stress biology · 2025Review
- Harnessing chemical communication in plant-microbiome and intra-microbiome interactions.Journal of Zhejiang University. Science. B · 2025Review
- Multi-Scale Remote-Sensing Phenomics Integrated with Multi-Omics: Advances in Crop Drought-Heat Stress Tolerance Mechanisms and Perspectives for Climate-Smart Agriculture.Plants (Basel, Switzerland) · 2025Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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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.