ArticleCommunications biology2019
Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 91 citations in OpenAlex.
- Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.Metabolites · 2026Review
- Metabolite correlation-based network analysis combined with machine learning techniques highlights LOX biosynthesis in Vanilla planifolia and Vanilla pompona source leaves.Scientific reports · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- Seed Metabolomic Landscape Reflecting Key Differential Metabolic Profiles Among Different Wheat Cultivars.Metabolites · 2025Article
- Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis.BMC pharmacology & toxicology · 2025Article
- From data to discovery: leveraging big data in plant natural products biosynthesis research.The Plant journal : for cell and molecular biology · 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
- Understanding metabolic diversification in plants: branchpoints in the evolution of specialized metabolism.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Examining predictors of cocaine withdrawal syndrome at the end of detoxification treatment in women with cocaine use disorder.Journal of psychiatric research · 2024Article
- Hemp Genome Editing-Challenges and Opportunities.Frontiers in genome editing · 2022Review
- Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance.Horticulture research · 2022Article
- Review
- Interpretable machine learning methods for predictions in systems biology from omics data.Frontiers in molecular biosciences · 2022Review
- Review
- A Global Metabolic Map Defines the Effects of a Si-Based Biostimulant on Tomato Plants under Normal and Saline Conditions.Metabolites · 2021Article
- Path to improving the life cycle and quality of genome-scale models of metabolism.Cell systems · 2021Review
- Why Has Metabolomics So Far Not Managed to Efficiently Contribute to the Improvement of Assisted Reproduction Outcomes? The Answer through a Review of the Best Available Current Evidence.Diagnostics (Basel, Switzerland) · 2021Article
- SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.BMC bioinformatics · 2021Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected.
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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.