ARGfore: A Multivariate Framework for Forecasting Antibiotic Resistance Gene Abundances Using Time-Series Metagenomic Datasets

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2026-02-23 DOI:10.1109/ACCESS.2026.3667074

Joung Min Choi;Monjura Afrin Rumi;Connor L. Brown;Peter J. Vikesland;Amy Pruden;Liqing Zhang

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引用次数: 0

Abstract

The global spread of antibiotic resistance presents a significant threat to human, animal, and plant health. Metagenomic sequencing is increasingly being utilized to profile antibiotic resistance genes (ARGs) in various environments, but presently a mechanism for predicting future trends in ARG occurrence patterns is lacking. Capability of forecasting ARG abundance trends could be extremely valuable towards informing policy and practice aimed at mitigating the evolution and spread of ARGs. Here we propose ARGfore, a multivariate forecasting model for predicting ARG abundances from time-series metagenomic data. ARGfore extracts features that capture inherent relationships among ARGs and is trained to recognize patterns in ARG trends and seasonality. ARGfore outperformed standard time-series forecasting methods, such as N-HiTS, LSTM, and ARIMA, exhibiting the lowest mean absolute percentage error when applied to different wastewater datasets. Additionally, ARGfore demonstrated enhanced computational efficiency, making it a promising candidate for a variety of ARG surveillance applications. The rapid prediction of future trends can facilitate early detection and deployment of mitigation efforts if necessary. ARGfore is publicly available at https://github.com/joungmin-choi/ARGfore

查看原文本刊更多论文

ARGfore：使用时间序列宏基因组数据集预测抗生素耐药基因丰度的多元框架

抗生素耐药性的全球蔓延对人类、动物和植物健康构成重大威胁。元基因组测序越来越多地用于分析各种环境中的抗生素耐药基因（ARGs），但目前缺乏预测ARG发生模式未来趋势的机制。预测ARG丰度趋势的能力对于为旨在减轻ARG演变和扩散的政策和实践提供信息非常有价值。本文提出了基于时间序列宏基因组数据预测ARG丰度的多元预测模型ARGfore。ARGfore提取捕获ARG之间内在关系的特征，并经过训练以识别ARG趋势和季节性的模式。ARGfore优于标准的时间序列预测方法，如N-HiTS、LSTM和ARIMA，当应用于不同的废水数据集时，显示出最低的平均绝对百分比误差。此外，ARGfore显示出更高的计算效率，使其成为各种ARG监测应用的有希望的候选者。对未来趋势的快速预测有助于及早发现并在必要时部署缓解措施。ARGfore可在https://github.com/joungmin-choi/ARGfore公开获取

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.