ReviewComputational and structural biotechnology journal2021
Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
96 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bibliometric analysis of the application of artificial intelligence techniques in bacteriology: a PRISMA-guided research agenda.Frontiers in microbiology · 2025Pooled it
- Predicting antifouling paint particle contamination based on 16S rRNA gene sequencing data using random forest-based machine learning.Microbiology spectrum · 2026Article
- Predicting the absence of World Health Organization fungal priority pathogens in agricultural soils using machine learning and ITS metabarcoding.Die Naturwissenschaften · 2026Article
- Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.Applied microbiology and biotechnology · 2026Review
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- RAS pathway activity subtypes identified by machine learning define prognostic and immune microenvironment characteristics in lung adenocarcinoma.Discover oncology · 2026Article
- Forging new insights in forensic microbiome studies: A 2000 to 2024 bibliometric analysis redefining the landscape.Medicine · 2026Review
- KeySDL: sparse dictionary learning for keystone microbe identification from steady-state observations using a dynamical-systems model.BioData mining · 2026Article
- Predicting oil contamination in water using machine learning on microbial compositions.PloS one · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- A genomic view of Earth's biomes.Nature reviews. Genetics · 2026Review
- Coevolution of Human Diet and Gut Microbiome: Implications for Nutrigenomics and Cross-Population Health.International journal of microbiology · 2026Review
- Decoding the rhizosphere microbiome againstFrontiers in microbiomes · 2026Review
- AI-Assisted Impedance Biosensing of Yeast Cell Concentration.Biosensors · 2025Article
- Best practices for developing microbiome-based disease diagnostic classifiers through machine learning.Gut microbes · 2025Article
- Integrating Microbiomes for Regenerative Food Systems: Recent Insights, Implementations, and Emerging Trends.Food science & nutrition · 2025Review
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- The microbiome in cancer.iMeta · 2025Review
- Identifying Optimal Machine Learning Approaches for Human Gut Microbiome (Shotgun Metagenomics) and Metabolomics Integration with Stable Feature Selection.bioRxiv : the preprint server for biology · 2025Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.
Indexed as
Identifiers
What OpenQuestion holds
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.