ArticlePLoS computational biology2024
Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Response to comment on "Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper".PLoS computational biology · 2026Article
- Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective.Antibiotics (Basel, Switzerland) · 2026Review
- Microbial biobanking: safeguarding the tiny treasures for sustainable human welfare.Folia microbiologica · 2026Review
- Structural modeling and docking analysis of canonical and novel resistance-associated missense mutations in Sudanese Escherichia coli.Scientific reports · 2026Article
- Leveraging developmental neurotoxicity data in zebrafish embryos through the use of artificial intelligence methods.Frontiers in toxicology · 2026Article
- Probiogenomics as a Computational Biotechnology Framework: Safety-Gated Genome Analytics for Candidate Probiotic Prioritization and Validation.Computational and structural biotechnology journal · 2026Review
- A leakage-aware genomic prediction pipeline for meropenem resistance inBioinformatics advances · 2026Article
- The WHO priority list of antibiotic-resistant bacteria: challenges and opportunities for next-generation antimicrobial development.Frontiers in pharmacology · 2026Review
- Comment on "Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper".PLoS computational biology · 2025Article
- Implementation of AI for predicting antibiotic resistance patterns: A hospital-based study.Bioinformation · 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
8 authors.
Funding
Abstract
Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most antibiotics, which means that for any given bacterial infection, the bacteria may be resistant to one or several antibiotics. It has been suggested that genomic sequencing and machine learning (ML) could make resistance testing more accurate and cost-effective. Given that ML is likely to become an ever more important tool in medicine, we believe that it is important for pre-health students and others in the life sciences to learn to use ML tools. This paper provides a step-by-step tutorial to train 4 different ML models (logistic regression, random forests, extreme gradient-boosted trees, and neural networks) to predict drug resistance for Escherichia coli isolates and to evaluate their performance using different metrics and cross-validation techniques. We also guide the user in how to load and prepare the data used for the ML models. The tutorial is accessible to beginners and does not require any software to be installed as it is based on Google Colab notebooks and provides a basic understanding of the different ML models. The tutorial can be used in undergraduate and graduate classes for students in Biology, Public Health, Computer Science, or related fields.
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.