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CARD*Shark：全面抗生素耐药性数据库文献管理的自动化优先级排序。

CARD*Shark: automated prioritization of literature curation for the Comprehensive Antibiotic Resistance Database.

机构信息

David Braley Centre for Antibiotic Discovery, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada.

Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main Street West, Hamilton, ON L8S 4L8, Canada.

出版信息

Database (Oxford). 2023 Apr 20;2023. doi: 10.1093/database/baad023.

DOI:10.1093/database/baad023
PMID:37079891
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10118295/
Abstract

Scientific literature is published at a rate that makes manual data extraction a highly time-consuming task. The Comprehensive Antibiotic Resistance Database (CARD) utilizes literature to curate information on antimicrobial resistance genes and to enable time-efficient triage of publications we have developed a classification algorithm for identifying publications describing first reports of new resistance genes. Trained on publications contained in the CARD, CARDShark downloads, processes and identifies publications recently added to PubMed that should be reviewed by biocurators. With CARDShark, we can minimize the monthly scope of articles a biocurator reviews from hundreds of articles to a few dozen, drastically improving the speed of curation while ensuring no relevant publications are overlooked. Database URL http://card.mcmaster.ca.

摘要

科学文献的发表速度非常快，使得手动数据提取成为一项非常耗时的任务。全面抗生素耐药性数据库（CARD）利用文献来整理关于抗菌药物耐药基因的信息，并使出版物的高效筛选成为可能。我们开发了一种分类算法，用于识别描述新耐药基因首次报告的出版物。在 CARD 中包含的出版物上进行训练，CARDShark 下载、处理并识别最近添加到 PubMed 中的出版物，这些出版物应由生物注释员进行审查。使用 CARDShark，我们可以将生物注释员每月需要审查的文章数量从数百篇减少到几十篇，在确保不忽略任何相关出版物的同时，极大地提高了注释的速度。数据库网址：http://card.mcmaster.ca。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c4d/10118295/542103c0196e/baad023f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c4d/10118295/0e474af9fd03/baad023f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c4d/10118295/542103c0196e/baad023f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c4d/10118295/0e474af9fd03/baad023f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c4d/10118295/542103c0196e/baad023f2.jpg

相似文献

[1]
CARD*Shark: automated prioritization of literature curation for the Comprehensive Antibiotic Resistance Database.

Database (Oxford). 2023-4-20

[2]
Accelerating literature curation with text-mining tools: a case study of using PubTator to curate genes in PubMed abstracts.

Database (Oxford). 2012-11-17

[3]
CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database.

Nucleic Acids Res. 2017-1-4

[4]
CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database.

Nucleic Acids Res. 2023-1-6

[5]
BioReader: a text mining tool for performing classification of biomedical literature.

BMC Bioinformatics. 2019-2-4

[6]
Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database.

PLoS One. 2013-4-17

[7]
LitCovid: an open database of COVID-19 literature.

Nucleic Acids Res. 2021-1-8

[8]
Integrating image caption information into biomedical document classification in support of biocuration.

Database (Oxford). 2020-1-1

[9]
A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions.

Database (Oxford). 2013-11-28

[10]
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.

Database (Oxford). 2022-8-31

引用本文的文献

[1]
New approaches to tackle a rising problem: Large-scale methods to study antifungal resistance.

PLoS Pathog. 2024-9-5

本文引用的文献

[1]
A Practical Approach for Predicting Antimicrobial Phenotype Resistance in Through Machine Learning Analysis of Genome Data.

Front Microbiol. 2022-3-2

[2]
Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.

Sci Rep. 2022-2-14

[3]
ResFinder - an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes.

Microb Genom. 2022-1

[4]
Prospects for Antibacterial Discovery and Development.

J Am Chem Soc. 2021-12-22

[5]
AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence.

Sci Rep. 2021-6-16

[6]
Identifying novel β-lactamase substrate activity through prediction of antimicrobial resistance.

Microb Genom. 2021-1

[7]
CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database.

Nucleic Acids Res. 2020-1-8

[8]
Applying Rapid Whole-Genome Sequencing To Predict Phenotypic Antimicrobial Susceptibility Testing Results among Carbapenem-Resistant Klebsiella pneumoniae Clinical Isolates.

Antimicrob Agents Chemother. 2018-12-21

[9]
The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee.

Clin Microbiol Infect. 2016-11-23

[10]
CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database.

Nucleic Acids Res. 2017-1-4

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