ReviewFrontiers in microbiology2022
Machine Learning Advances in Microbiology: A Review of Methods and Applications.
Review in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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
54 citing papers in PubMed.
- Prognostic prediction of hepatoblastoma in children: development and validation of machine learning models-an SEER-based study.Updates in surgery · 2026Article
- Microbial biobanking: safeguarding the tiny treasures for sustainable human welfare.Folia microbiologica · 2026Review
- Utilising unsupervised machine learning to predict outbreaks of respiratory tract infections in acute Irish hospitals (2016-2021).Public health in practice (Oxford, England) · 2026Article
- Navigating complexity: key considerations for studying fungal-bacterial interactions.mSystems · 2026Article
- Review
- Development of a machine learning-based screening model for the risk of depression among the elderly in China.BMC geriatrics · 2026Article
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- Genomic Epidemiology and Machine Learning-Based Drug Discovery for Antimicrobial Resistant Diarrheagenic Escherichia coli.MicrobiologyOpen · 2026Article
- Automated microbial recognition from gram-stained micrographs via CNN and metadata decision support.Frontiers in cellular and infection microbiology · 2026Article
- Interpretable learning algorithms enable pathogenic potential assessment and virulence-associated gene discovery ofFrontiers in microbiology · 2026Article
- Evolving microbiology laboratories: mastering automated culture-based processes and molecular assays, an institutional experience.Frontiers in cellular and infection microbiology · 2026Review
- TRACE: reconstructing fragmented microbial landscapes for high-resolution antimicrobial resistance surveillance.Frontiers in microbiology · 2026Article
- Uncovering Key Genes in Neurodegenerative Diseases Through Unsupervised Learning: A Variance-Based LSTM and Enrichment Analysis Approach.Advances in experimental medicine and biology · 2026Article
- Proteomic Sample Preparation for the Petroleum Industry: A Biocorrosion Case Study.Advances in experimental medicine and biology · 2026Review
- The evolution of diagnostic microbiology: integrating culture-based methods and genomic advances.PeerJ · 2026Review
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- A machine learning model and molecular clusters of epigenetic chromatin regulators in tuberculosis based on bioinformatics and clinical samples.Scientific reports · 2025Article
- Application of Machine Learning in Food Safety Risk Assessment.Foods (Basel, Switzerland) · 2025Review
- Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.BMC microbiology · 2025Article
- AI-Powered Workflow for Constructing Organic Materials Databases from the Literature: Integrating Large Language Models.ACS omega · 2025Article
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 play an important role in natural material and elemental cycles. Many common and general biology research techniques rely on microorganisms. Machine learning has been gradually integrated with multiple fields of study. Machine learning, including deep learning, aims to use mathematical insights to optimize variational functions to aid microbiology using various types of available data to help humans organize and apply collective knowledge of various research objects in a systematic and scaled manner. Classification and prediction have become the main achievements in the development of microbial community research in the direction of computational biology. This review summarizes the application and development of machine learning and deep learning in the field of microbiology and shows and compares the advantages and disadvantages of different algorithm tools in four fields: microbiome and taxonomy, microbial ecology, pathogen and epidemiology, and drug discovery.
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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.