ArticleScientific reports2019
Interpretable genotype-to-phenotype classifiers with performance guarantees.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.
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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.
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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.
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Who cites it
61 citing papers in PubMed, 3 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Pooled it
- Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.The Journal of antimicrobial chemotherapy · 2020Pooled it
- Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.PLoS computational biology · 2019Pooled it
- Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus.Interdisciplinary sciences, computational life sciences · 2026Article
- Article
- Uncovering species- and drug-class-specific antimicrobial resistance mechanisms from large-scale whole-genome sequencing data using discordance analysis and machine learning.Briefings in bioinformatics · 2026Article
- resLens: genomic language models to enhance antibiotic resistance gene detection.npj antimicrobials and resistance · 2026Article
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Evaluation of a machine learning system for genomic antimicrobial susceptibility determination on a clinically representative test set.Microbiology spectrum · 2026Article
- StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens.Journal of biosciences · 2026Article
- stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.Bioinformatics advances · 2026Article
- Minimal gene signatures enable high-accuracy prediction of antibiotic resistance in Pseudomonas aeruginosa.NPJ systems biology and applications · 2025Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- On Selecting Robust Approaches for Learning Predictive Biomarkers in Metabolomics Data Sets.Analytical chemistry · 2025Article
- Bacterial genome-wide association studies: exploring the genetic variation underlying bacterial phenotypes.Applied and environmental microbiology · 2025Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model.Briefings in bioinformatics · 2024Article
- Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
1 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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use in this setting. The high dimensionality of the data tends to hinder generalization and challenges the scalability of most learning algorithms. Additionally, most algorithms produce models that are complex and difficult to interpret. We alleviate these limitations by proposing strong performance guarantees, based on sample compression theory, for rule-based learning algorithms that produce highly interpretable models. We show that these guarantees can be leveraged to accelerate learning and improve model interpretability. Our approach is validated through an application to the genomic prediction of antimicrobial resistance, an important public health concern. Highly accurate models were obtained for 12 species and 56 antibiotics, and their interpretation revealed known resistance mechanisms, as well as some potentially new ones. An open-source disk-based implementation that is both memory and computationally efficient is provided with this work. The implementation is turnkey, requires no prior knowledge of machine learning, and is complemented by comprehensive tutorials.
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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.