ReviewFEMS microbiology reviews2021
Machine learning and applications in microbiology.
Review in FEMS microbiology reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 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
93 citing papers in PubMed.
- Fungal Growth Risk Prediction and Optimal Regulation Method for Food Storage Based on the Forward Reachable Set.Foods (Basel, Switzerland) · 2026Article
- Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective.Antibiotics (Basel, Switzerland) · 2026Review
- Karyoptosis mediates cell death and neurodegeneration upon proteotoxic stress.Nature communications · 2026Article
- 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
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Navigating the promise and pitfalls of artificial intelligence.Nature microbiology · 2026Article
- COApplied microbiology and biotechnology · 2026Review
- Species-specific discrimination of bacterial biofilms using a ratiometric fluorescence sensor array and machine learning.Sensors & diagnostics · 2026Article
- Machine learning mortality prediction model for cyclosporine therapy in pediatric aplastic anemia.Annals of hematology · 2026Article
- Hybrid deep learning framework MedFusionNet assists multilabel biomedical risk stratification from imaging and tabular data.Communications medicine · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- Omics and Multiomics-Based Diagnostics for Invasive Candidiasis: Toward Precision Medicine.Molecular & cellular proteomics : MCP · 2025Review
- Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy.Microbiology spectrum · 2025Article
- Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.bioRxiv : the preprint server for biology · 2025Article
- Parasitology of the twenty-first century: are we moving in the right direction?Journal of medical microbiology · 2025Review
- AI-enhanced bioprocess technologies: machine learning implementations from upstream to downstream operations.World journal of microbiology & biotechnology · 2025Review
- Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (Foods (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings.Journal of pharmacy & bioallied sciences · 2025Article
- Characterizing low femoral neck BMD in Qatar Biobank participants using machine learning models.BMC musculoskeletal disorders · 2025Article
33 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.
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.