ReviewNature reviews. Microbiology2024
Machine learning for microbiologists.
Review in Nature reviews. Microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 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
101 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review: Long-Read Sequencing in Algal Studies.International journal of molecular sciences · 2026Pooled it
- Microbiome-based diagnostic biomarkers in pancreatic ductal adenocarcinoma: Current evidence and translational challenges (Review).Oncology letters · 2026Review
- Fermentation-driven valorization of cereal by-products: Mechanisms, functional applications, and emerging AI-assisted approaches.Food chemistry: X · 2026Review
- Microbial Colony Image Enhancement Algorithm Based on Composite Adversarial D-DCGAN Neural Network.Microorganisms · 2026Article
- High-resolution profiling of bacterial transcriptomes by SEnd-seq.The Biochemical journal · 2026Review
- Application of Supervised Machine Learning Algorithms to Morphological Classification of Bufo bufo (Linnaeus, 1758) and B. verrucosissimus (Pallas, 1814) From Türkiye.Journal of morphology · 2026Article
- Living Inorganic Nanomaterials: Design, Preparation, and Biomedical Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Beyond omics: From descriptive profiling to causal and scalable design of fermentation microbiomes.iMeta · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- Identification of candidate genes and metabolites associated with lactation performance in Kazakh mares using blood multi-omics and machine learning.BMC genomics · 2026Article
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.iScience · 2026Article
- The oral microbiome is associated with the diagnosis, prognosis and radiotherapy sensitivity of esophageal cancer.Journal of translational medicine · 2026Article
- A shape optimization method for resistance reduction of local piping components with multiscale validation.Communications engineering · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Integrated analysis of human-mouse gut microbiota in RSV infection based on machine learning.BMC microbiology · 2026Article
- Prediction of antimicrobial minimum inhibitory concentration from bacterial genomes using a scalable and interpretable machine learning approach.npj antimicrobials and resistance · 2026Article
- Microbial signatures define the ecosystem functions of the pelagic microbiome in a basin-scale, Southwest Atlantic Ocean.Scientific reports · 2026Article
- The Women's Microbiome: Molecular Insights, Clinical Gaps, and Future Frontiers in Precision Health with Implications for Gulf Cooperation Council Populations.International journal of molecular sciences · 2026Review
- Shotgun metagenomic analysis of the oral microbiomes of children with noma.PLoS neglected tropical diseases · 2026Article
41 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
Abstract
Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.
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.