Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database

Domain:

healthcare

Record type:

datasetsoftware
Creator:
BriWilRomKea
Publisher:
Oxf
Host:
Abstract The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded β-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.

Visit

doi.org

Licenses

https://creativecommons.org/licenses/by/4.0/

Similar

Database for Enhancing forage yield prediction in Benin using NDVI and ensemble machine learning approachesThe Comprehensive Machine Learning Analytics for Heart FailureA clinical support system for classification and prediction of depression using machine learning methodsOptimizing energy forecasts at Boma for 2023 to 2053 Using machine learning techniques of the PSO algorithmArtificial Intelligence and Machine Learning for Antimicrobial Resistance Prediction: A Scoping Review and Implications for Low- and Middle-Income CountriePopulation-Structure-Aware Machine Learning for Antimicrobial Resistance Prediction from Bacterial Accessory Genomes: A Benchmark on Escherichia coli Ciprofloxacin Resistance

Database for Enhancing forage yield prediction in Benin using NDVI and ensemble machine learning approaches

Database for Enhancing forage yield prediction in Benin using NDVI and ensemble machine learning app

The Comprehensive Machine Learning Analytics for Heart Failure

Background: Early detection of heart failure is the basis for better medical treatment and prognosis

A clinical support system for classification and prediction of depression using machine learning methods

Abstract The health sector collects a very large amount of data, hence the diagnostic process proce

Optimizing energy forecasts at Boma for 2023 to 2053 Using machine learning techniques of the PSO algorithm

This research was conducted to optimize energy consumption forecasting in the commune of Boma, in th

Artificial Intelligence and Machine Learning for Antimicrobial Resistance Prediction: A Scoping Review and Implications for Low- and Middle-Income Countrie

This ongoing scoping review aims to systematically map the evidence on artificial intelligence (AI)

Population-Structure-Aware Machine Learning for Antimicrobial Resistance Prediction from Bacterial Accessory Genomes: A Benchmark on Escherichia coli Ciprofloxacin Resistance

Abstract

Antimicrobial res