ArticleBiology2020
Amino Acid
Article in Biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Differentiating commensal and disease-associatedFrontiers in veterinary science · 2026Article
- Machine Learning Prediction of Multidrug Resistance in Swine-DerivedVeterinary sciences · 2025Article
- Taxonomic quasi-primes: peptides charting lineage-specific adaptations and disease-relevant loci.Protein science : a publication of the Protein Society · 2025Article
- Unraveling diversity by isolating peptide sequences specific to distinct taxonomic groups.bioRxiv : the preprint server for biology · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection.Briefings in bioinformatics · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Effect of tokenization on transformers for biological sequences.Bioinformatics (Oxford, England) · 2024Article
- A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria.Frontiers in antibiotics · 2024Article
- TCRpred: incorporating T-cell receptor repertoire for clinical outcome prediction.Frontiers in genetics · 2024Article
- Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China.Nature food · 2023Article
- Antimicrobial resistance and machine learning: past, present, and future.Frontiers in microbiology · 2023Article
- aaHash: recursive amino acid sequence hashing.Bioinformatics advances · 2023Article
- A comparison of various feature extraction and machine learning methods for antimicrobial resistance prediction inFrontiers in antibiotics · 2023Article
- Article
- Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning.Computers in biology and medicine · 2022Article
- Prediction of antimicrobial minimal inhibitory concentrations forSaudi journal of biological sciences · 2022Article
- Whole-genome sequencing and gene sharing network analysis powered by machine learning identifies antibiotic resistance sharing between animals, humans and environment in livestock farming.PLoS computational biology · 2022Article
- Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.International journal of molecular sciences · 2021Article
- A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.The Journal of antibiotics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide
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.