國家衛生研究院 NHRI:Item 3990099045/15833
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 12145/12927 (94%)
造访人次 : 852310      在线人数 : 1522
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于NHRI ‧ 管理 到手机版


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.nhri.org.tw/handle/3990099045/15833


    题名: ARGNet: Using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences
    作者: Pei, Y;Shum, MHH;Liao, YS;Leung, VW;Gong, YN;Smith, DK;Yin, XL;Guan, Y;Luo, RB;Zhang, T;Lam, TTY
    贡献者: National Institute of Infectious Diseases and Vaccinology
    摘要: Background Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.Results In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.Conclusions ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet, with an online service provided at https://ARGNet.hku.hk. 7nR91jX7ySRMRPZrE5tsTy Video AbstractConclusions ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet, with an online service provided at https://ARGNet.hku.hk. 7nR91jX7ySRMRPZrE5tsTy Video Abstract
    日期: 2024-05-09
    關聯: Microbiome. 2024 May 09;12:Article number 84.
    Link to: http://dx.doi.org/10.1186/s40168-024-01805-0
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2049-2618&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:001217611200002
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85192569064
    显示于类别:[其他] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    ISI001217611200002.pdf4375KbAdobe PDF64检视/开启


    在NHRI中所有的数据项都受到原著作权保护.

    TAIR相关文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈