ReviewFrontiers in microbiology2019
Application of Machine Learning in Microbiology.
Review in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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.
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
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Microbial biobanking: safeguarding the tiny treasures for sustainable human welfare.Folia microbiologica · 2026Review
- AI-enhanced virtual laboratories improve learning outcomes and student engagement in food microbiology education.Scientific reports · 2026Article
- From microbial diversity to functional potential using dimensionality reduction.Frontiers in microbiology · 2026Article
- Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights.Frontiers in microbiology · 2026Review
- Predicting oil contamination in water using machine learning on microbial compositions.PloS one · 2026Article
- Phylo-Spec: a phylogeny-fusion deep learning model advances microbiome status identification.mSystems · 2025Article
- Knowledge graph embedding for predicting and analyzing microbial interactions.Scientific reports · 2025Article
- Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy.Microbiology spectrum · 2025Article
- Harnessing artificial intelligence for detection of pancreatic cancer: a machine learning approach.Clinical and experimental medicine · 2025Article
- Artificial intelligence: the human response to approach the complexity of big data in biology.GigaScience · 2025Review
- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Article
- Diagnostic Stewardship in Clinical Microbiology: An Indispensable Component of Patient Care.Infectious disorders drug targets · 2025Review
- Extreme learning machine for identifying soil-dwelling microorganisms cultivated on agar media.Scientific reports · 2024Article
- Predicting MHD mixed convection in a semicircular cavity with hybrid nanofluids using AI.Heliyon · 2024Article
- Machine learning classification of archaea and bacteria identifies novel predictive genomic features.BMC genomics · 2024Article
- Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers inApplied and environmental microbiology · 2024Article
- Lactic acid bacteria in some Indian fermented foods and their predictive functional profiles.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024Article
- Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology.mBio · 2024Review
14 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microorganisms are ubiquitous and closely related to people's daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology.
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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.