ArticleNature ecology & evolution2024
Identifying keystone species in microbial communities using deep learning.
Article in Nature ecology & evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
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- Enhanced soil health and greenhouse gas mitigation potential are linked to microbial network restructuring under long-term organic farming.Fundamental research · 2026Article
- Deciphering microbial community dynamics using cross-sectional data-informed NeuralODE.Microbiome · 2026Article
- Horizontal Gene Transfer and Genome Rearrangements Shape Bacterial Adaptation for Bioremediation.Environmental microbiology · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- Article
- Microbiologically influenced corrosion (MIC) potential of bentonite microorganisms: implications for a deep geological repository for nuclear waste.World journal of microbiology & biotechnology · 2026Article
- Probiotics and Postbiotics in Life-Style Disease Management: A Comprehensive Review on the Technologies in the Era of Omics and Artificial Intelligence.Probiotics and antimicrobial proteins · 2026Review
- Rhizosphere Functional Plasticity and the Keystone TaxonPlants (Basel, Switzerland) · 2026Article
- Research advances in the identification of gut keystone bacteria and formulation of suitable dietary intervention strategies for restoring gut homeostasis.NPJ biofilms and microbiomes · 2026Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Omics-Guided Construction of Microbial Consortia for Reproducible Traditional Fermented Foods and Beverages.Foods (Basel, Switzerland) · 2026Review
- Precisely designed keystone metabolites boost shrimp disease resistance by recruiting symbionts via the lipoxin A4-AP-1 pathway.Microbiome · 2026Article
- Keystone concept revisited: insights into microbial community dynamics and control.Nature reviews. Microbiology · 2026Review
- Altered microbial cargo in fecal microbiome-derived outer membrane vesicles as novel biomarkers for vascular dementia.BMC microbiology · 2026Article
- MGM as a Large-Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Tools and approaches to study the human gut virome: from the bench to bioinformatics.mSystems · 2026Review
- Artificial Intelligence for Microbial Isolation and Cultivation: Progress and Challenges.Microorganisms · 2026Review
- Innovative Systems Biology in Baijiu Fermentation: Unveiling Omics Landscapes and Microbial Synergy.Foods (Basel, Switzerland) · 2026Review
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Abstract
Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.
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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.