ArticleBMC genomics2016
Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
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Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Artificial Intelligence in Rapid Diagnostics for Antimicrobial Resistance: From Bench to Bedside.Health science reports · 2026Article
- 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
- MKMC enables reference-free transcriptomic analysis using k-mer representations.bioRxiv : the preprint server for biology · 2026Article
- Agnostic material classification using differential de Bruijn graphs of DNA imprints.bioRxiv : the preprint server for biology · 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
- Integrating Phenotypic and Genomic Data with Machine Learning to Predict Antimicrobial Resistance and Identify Genetic Biomarkers inInternational journal of environmental research and public health · 2026Article
- AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.Nature communications · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Leveraging Artificial Intelligence to Advance Bioinformatics in Africa: Opportunities, Challenges, and Ethical Considerations in Combating Antimicrobial Resistance.Bioinformatics and biology insights · 2026Article
- Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms.NPJ biofilms and microbiomes · 2025Article
- deepBreaks identifies and prioritizes genotype-phenotype associations using machine learning.Scientific reports · 2025Article
- Machine Learning Prediction of Multidrug Resistance in Swine-DerivedVeterinary sciences · 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
- Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance.npj antimicrobials and resistance · 2025Review
- Extracting a COVID-19 signature from a multi-omic dataset.Frontiers in bioinformatics · 2025Article
- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024Review
20 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies.
resultsThe method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery.
conclusionsOur method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes ( http://github.com/aldro61/kover/ ).
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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.