Journal article
A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling
Briefings in bioinformatics, v 23(3)
10 Mar 2022
PMID: 35272349
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can identify ARGs relatively more accurately, the identifying process is time-consuming and specialized knowledge is required. With the rapid development of whole genome sequencing technology, researchers attempt to identify ARGs by computing sequence similarity from public databases. However, these computational methods might fail to detect ARGs due to the low sequence identity to known ARGs. Moreover, existing methods cannot effectively address the issue of multidrug resistance prediction for ARGs, which is a great challenge to clinical treatments. To address the challenges, we propose an end-to-end multi-label learning framework for predicting ARGs. More specifically, the task of ARGs prediction is modeled as a problem of multi-label learning, and a deep neural network-based end-to-end framework is proposed, in which a specific loss function is introduced to employ the advantage of multi-label learning for ARGs prediction. In addition, a dual-view modeling mechanism is employed to make full use of the semantic associations among two views of ARGs, i.e. sequence-based information and structure-based information. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs prediction.
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Details
- Title
- A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling
- Creators
- Weizhong Zhao - Central China Normal UniversityShujie Luo - Central China Normal UniversityHaifang Wu - Central China Normal UniversityXingpeng Jiang - Central China Normal UniversityTingting He - Central China Normal UniversityXiaohua Hu - Drexel University
- Publication Details
- Briefings in bioinformatics, v 23(3)
- Publisher
- Oxford Univ Press
- Number of pages
- 12
- Grant note
- 61532008; 61872157; 61932008 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) 2019010701011392 / Wuhan Science and Technology Program MIMS19-02 / Research Fund of Guangxi Key Lab of Multi-source Information Mining Security kx201905 / Guangxi Key Laboratory of Trusted Software 2020BAB017 / Key Research and Development Program of Hubei Province CCNU19TD004 / Fundamental Research Funds for the Central Universities
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000767443700001
- Scopus ID
- 2-s2.0-85130767529
- Other Identifier
- 991019167570104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Mathematical & Computational Biology