ArticleBMC genomics2024
Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Alternative Artemisinin Production Strategies.International journal of molecular sciences · 2025Pooled it
- Challenges and Future Directives of Synthetic Biology in Engineering Plant-Microbe Partnerships for Sustainable Agriculture.Biotechnology and bioengineering · 2026Review
- The Convergence of Biotechnological Strategies in Medicinal Plants: From Individual Approaches Toward an Integrated Systems Framework.International journal of molecular sciences · 2026Review
- Integrating Biosynthetic, Genomic and Ecological Open Data for Medicinal Plant Research: A Leakage-Aware Evidence-Prioritization Framework.International journal of molecular sciences · 2026Article
- Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.Functional & integrative genomics · 2026Review
- Advances in Functional Genomics and Biotechnology for Enhancing Therapeutic Potential of Medicinal Plants.International journal of molecular sciences · 2026Review
- Epigenetic regulation of mycorrhizal symbioses: from plastic responses to transgenerational legacies.The New phytologist · 2026Review
- Plant Growth-Promoting Microorganisms Mediate Plant Metabolic Reprogramming to Manage the Rhizospheric Microbiome.Microorganisms · 2026Review
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- AI-driven integration and optimization of medicinal plant multi-omics metabolic networks.Frontiers in plant science · 2026Review
- A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.Frontiers in plant science · 2026Review
- 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
- Metabolic Engineering of Terpenoid Biosynthesis in Medicinal Plants: From Genomic Insights to Biotechnological Applications.Current issues in molecular biology · 2025Review
- Antioxidant Defense Systems in Plants: Mechanisms, Regulation, and Biotechnological Strategies for Enhanced Oxidative Stress Tolerance.Life (Basel, Switzerland) · 2025Review
- Integrating multi-omics and machine learning for disease resistance prediction in legumes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- 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
- New insights in metabolism modelling to decipher plant-microbe interactions.The New phytologist · 2025Review
- Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.Journal of translational medicine · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundPlant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on discovering novel metabolites and their biosynthetic genes, the resolution of metabolic pathways and identified biosynthetic genes was limited by rudimentary analysis approaches and enormous number of candidate genes.
resultsHere we integrated state-of-the-art automated machine learning (ML) frame AutoGluon-Tabular and multi-omics data from Arabidopsis to predict genes encoding enzymes involved in biosynthesis of plant specialized metabolite (PSM), focusing on the three main PSM categories: terpenoids, alkaloids, and phenolics. We found that the related features of genomics and proteomics were the top two crucial categories of features contributing to the model performance. Using only these key features, we built a new model in Arabidopsis, which performed better than models built with more features including those related with transcriptomics and epigenomics. Finally, the built models were validated in maize and tomato, and models tested for maize and trained with data from two other species exhibited either equivalent or superior performance to intraspecies predictions.
conclusionsOur external validation results in grape and poppy on the one hand implied the applicability of our model to the other species, and on the other hand showed enormous potential to improve the prediction of enzymes synthesizing PSM with the inclusion of valid data from a wider range of species.
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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 Socratic graph.