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Journal article
Causal-ARG: A Causality-guided Framework for Annotating Properties of Antibiotic Resistance Genes
Bioinformatics (Oxford, England), v 40(4), btae180
03 Apr 2024
PMID: 38569882
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance.
In this paper, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is employed to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs.
The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.
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Details
- Title
- Causal-ARG: A Causality-guided Framework for Annotating Properties of Antibiotic Resistance Genes
- Creators
- Weizhong Zhao (Corresponding Author) - Central China Normal UniversityJunze Wu - Central China Normal UniversityXingpeng Jiang - Central China Normal UniversityTingting He - Central China Normal UniversityXiaohua Hu - Drexel University
- Publication Details
- Bioinformatics (Oxford, England), v 40(4), btae180
- Publisher
- Oxford University Press
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001234950300002
- Scopus ID
- 2-s2.0-85191041871
- Other Identifier
- 991021866846304721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability