• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

相似文献

1
Editorial: Computational Predictions, Dynamic Tracking, and Evolutionary Analysis of Antibiotic Resistance Through the Mining of Microbial Genomes and Metagenomic Data.社论：通过挖掘微生物基因组和宏基因组数据对抗生素耐药性进行计算预测、动态追踪及进化分析
Front Microbiol. 2022 Apr 4;13:880967. doi: 10.3389/fmicb.2022.880967. eCollection 2022.
2
Editorial: Computational predictions, dynamic tracking, and evolutionary analysis of antibiotic resistance through the mining of microbial genomes and metagenomic data, volume II.社论：通过挖掘微生物基因组和宏基因组数据对抗生素耐药性进行计算预测、动态追踪和进化分析，第二卷
Front Microbiol. 2023 May 10;14:1203297. doi: 10.3389/fmicb.2023.1203297. eCollection 2023.
3
Graph-Based Approaches Significantly Improve the Recovery of Antibiotic Resistance Genes From Complex Metagenomic Datasets.基于图谱的方法显著提高了从复杂宏基因组数据集中恢复抗生素抗性基因的能力。
Front Microbiol. 2021 Oct 6;12:714836. doi: 10.3389/fmicb.2021.714836. eCollection 2021.
4
Source identification of antibiotic resistance genes in a peri-urban river using novel crAssphage marker genes and metagenomic signatures.利用新型 crAssphage 标记基因和宏基因组特征鉴定城市周边河流中的抗生素耐药基因。
Water Res. 2019 Dec 15;167:115098. doi: 10.1016/j.watres.2019.115098. Epub 2019 Sep 17.
5
Intestinal microbiota domination under extreme selective pressures characterized by metagenomic read cloud sequencing and assembly.肠道微生物群落在具有宏基因组读段云测序和组装特征的极端选择压力下占主导地位。
BMC Bioinformatics. 2019 Dec 2;20(Suppl 16):585. doi: 10.1186/s12859-019-3073-1.
6
Parallel-META: efficient metagenomic data analysis based on high-performance computation.并行元基因组分析：基于高性能计算的高效宏基因组数据分析
BMC Syst Biol. 2012;6 Suppl 1(Suppl 1):S16. doi: 10.1186/1752-0509-6-S1-S16. Epub 2012 Jul 16.
7
Source tracking of antibiotic resistance genes in the environment - Challenges, progress, and prospects.环境中抗生素耐药基因的溯源 - 挑战、进展与展望。
Water Res. 2020 Oct 15;185:116127. doi: 10.1016/j.watres.2020.116127. Epub 2020 Aug 2.
8
MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm.MaxBin：一种基于期望最大化算法的自动分类方法，可从宏基因组中回收单个基因组。
Microbiome. 2014 Aug 1;2:26. doi: 10.1186/2049-2618-2-26. eCollection 2014.
9
Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data.抗生素耐药组的生物信息学分析方法概述：基于基因组和宏基因组数据分析。
J Microbiol. 2021 Mar;59(3):270-280. doi: 10.1007/s12275-021-0652-4. Epub 2021 Feb 23.
10
Application of computational approaches to analyze metagenomic data.应用计算方法分析宏基因组数据。
J Microbiol. 2021 Mar;59(3):233-241. doi: 10.1007/s12275-021-0632-8. Epub 2021 Feb 10.

引用本文的文献

1
Metaomics in Clinical Laboratory: Potential Driving Force for Innovative Disease Diagnosis.临床实验室中的元组学：创新疾病诊断的潜在驱动力
Front Microbiol. 2022 Jun 17;13:883734. doi: 10.3389/fmicb.2022.883734. eCollection 2022.

本文引用的文献

1
Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study.通过计算分析表面增强拉曼光谱对耐碳青霉烯和碳青霉烯敏感肺炎克雷伯菌菌株的区分：一项初步研究。
Microbiol Spectr. 2022 Feb 23;10(1):e0240921. doi: 10.1128/spectrum.02409-21. Epub 2022 Feb 2.
2
Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Species.机器学习算法对临床物种表面增强拉曼光谱的比较分析
Front Microbiol. 2021 Aug 31;12:696921. doi: 10.3389/fmicb.2021.696921. eCollection 2021.
3
Computational resources in the management of antibiotic resistance: Speeding up drug discovery.计算资源在抗生素耐药性管理中的应用：加速药物研发。
Drug Discov Today. 2021 Sep;26(9):2138-2151. doi: 10.1016/j.drudis.2021.04.016. Epub 2021 Apr 20.
4
Metagenomic Approaches to Analyze Antimicrobial Resistance: An Overview.宏基因组学方法分析抗菌药物耐药性：综述
Front Genet. 2021 Jan 18;11:575592. doi: 10.3389/fgene.2020.575592. eCollection 2020.
5
Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research.机器学习在抗菌药物耐药性问题中的应用：转化研究的新兴模型。
J Clin Microbiol. 2021 Jun 18;59(7):e0126020. doi: 10.1128/JCM.01260-20.
6
Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of From Whole Genome Sequences.基于全基因组序列预测抗菌药物耐药性的机器学习模型评估
Front Microbiol. 2020 Feb 6;11:48. doi: 10.3389/fmicb.2020.00048. eCollection 2020.
7
Identification and reconstruction of novel antibiotic resistance genes from metagenomes.从宏基因组中鉴定和重建新型抗生素耐药基因。
Microbiome. 2019 Apr 1;7(1):52. doi: 10.1186/s40168-019-0670-1.
8
Dissemination of Antimicrobial Resistance in Microbial Ecosystems through Horizontal Gene Transfer.通过水平基因转移在微生物生态系统中传播抗菌药物耐药性
Front Microbiol. 2016 Feb 19;7:173. doi: 10.3389/fmicb.2016.00173. eCollection 2016.

Editorial: Computational Predictions, Dynamic Tracking, and Evolutionary Analysis of Antibiotic Resistance Through the Mining of Microbial Genomes and Metagenomic Data.

作者信息

Wang Liang, Tay Alfred Chin Yen, Li Jian, Zhao Qi

机构信息

Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China.

Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, China.

出版信息

Front Microbiol. 2022 Apr 4;13:880967. doi: 10.3389/fmicb.2022.880967. eCollection 2022.

DOI:10.3389/fmicb.2022.880967
PMID:35444627
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9014298/
Abstract
摘要