ReviewEnvironmental microbiome2021
Deep learning approaches for natural product discovery from plant endophytic microbiomes.
Review in Environmental microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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.
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Who cites it
29 citing papers in PubMed, 61 citations in OpenAlex.
- Review
- Chemical Signaling and Metabolomic Crosstalk in Endophytic Fungi-Medicinal Plant Symbioses for Natural Product Discovery and Sustainable Bioproduction.Metabolites · 2026Review
- Diverse banana endophytes reveal potential genotype-driven community structure affected by domestication.Frontiers in microbiology · 2026Article
- Conserved gene clusters in entomopathogenic filamentous fungi.Genetics and molecular biology · 2026Article
- Comparison of Rhizosphere Fungal Community Changes in Healthy and Yellowing-Leaf-Disease-Affected Areca Palms by High-Throughput Sequencing Technology.Journal of fungi (Basel, Switzerland) · 2025Article
- Gradient responsive regularization: a deep learning framework for codon frequency based classification of evolutionarily conserved genes.BMC genomic data · 2025Article
- Genomic and Metabolomic Analysis of the Endophytic FungusAntibiotics (Basel, Switzerland) · 2025Article
- Role of Endophytic Fungi in the Biosynthesis of Metal Nanoparticles and Their Potential as Nanomedicines.Journal of functional biomaterials · 2025Review
- Defining the limits of plant chemical space: challenges and estimations.GigaScience · 2025Article
- Strong pairwise interactions do not drive interactions in a plant leaf associated microbial community.ISME communications · 2025Article
- Endophytic fungi: A future prospect for breast cancer therapeutics and drug development.Heliyon · 2024Review
- Antimicrobial Action Mechanisms of Natural Compounds Isolated from Endophytic Microorganisms.Antibiotics (Basel, Switzerland) · 2024Review
- Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning.Microbiology spectrum · 2024Article
- Comparative Analysis of Rhizosphere and Endosphere Fungal Communities in Healthy and Diseased Faba Bean Plants.Journal of fungi (Basel, Switzerland) · 2024Article
- A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters.Journal of chemical ecology · 2023Article
- Global analyses of biosynthetic gene clusters in phytobiomes reveal strong phylogenetic conservation of terpenes and aryl polyenes.mSystems · 2023Article
- Role of Arbuscular Mycorrhizal Fungi in Regulating Growth, Enhancing Productivity, and Potentially Influencing Ecosystems under Abiotic and Biotic Stresses.Plants (Basel, Switzerland) · 2023Review
- Review
- Modern Trends in Natural Antibiotic Discovery.Life (Basel, Switzerland) · 2023Review
- Metapangenomics of wild and cultivated banana microbiome reveals a plethora of host-associated protective functions.Environmental microbiome · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich 'in planta' community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40793-021-00375-0.
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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.