ReviewFrontiers in genetics2021
Application of Deep Learning in Plant-Microbiota Association Analysis.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Challenges and Future Directives of Synthetic Biology in Engineering Plant-Microbe Partnerships for Sustainable Agriculture.Biotechnology and bioengineering · 2026Review
- Review
- Soil microbiome prediction using traditional machine learning and deep learning models.Scientific reports · 2026Article
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
- Phytomycobiomes and Ecosystem Services: Mechanisms, Evidence and Routes to Application.Journal of fungi (Basel, Switzerland) · 2025Review
- The Rice-Microbe Nexus: Unlocking Productivity Through Soil Science.Rice (New York, N.Y.) · 2025Review
- Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function by FISH-scRACS-seq.Innovation (Cambridge (Mass.)) · 2025Article
- Sustainable microbial solutions for managing fungal threats in wheat: progress and future directions.World journal of microbiology & biotechnology · 2025Review
- Seed tuber microbiome can predict growth potential of potato varieties.Nature microbiology · 2025Article
- From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases.BioData mining · 2024Article
- Interpretable machine learning decodes soil microbiome's response to drought stress.Environmental microbiome · 2024Article
- Exploring Cereal Metagenomics: Unravelling Microbial Communities for Improved Food Security.Microorganisms · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2023Article
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.Current opinion in plant biology · 2023Review
- Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest.Frontiers in genetics · 2023Article
- Exercise and microbiome: From big data to therapy.Computational and structural biotechnology journal · 2023Review
- Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes.Frontiers in genetics · 2023Review
- Dynamic Network Biomarker Analysis Reveals the Critical Phase Transition of Fruit Ripening in Grapevine.Genes · 2022Article
- Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant-microbiome correlation studies. We also introduce the application cases of different models in plant-microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.
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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.