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Updated: May 28, 2025
06:56
Detection of Horizontal Gene Transfer Mediated by Natural Conjugative Plasmids in E. coli
Published on: March 24, 2023
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Current state and future prospects of Horizontal Gene Transfer detection
Andre Jatmiko Wijaya1,2,3, Aleksandar Anžel1, Hugues Richard3
1Center for Artificial Intelligent in Public Health Research (ZKI-PH), Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany.
NAR Genomics and Bioinformatics
|February 12, 2025
Summary
Artificial intelligence (AI) is increasingly used for detecting Horizontal Gene Transfer (HGT), a key factor in antimicrobial resistance (AMR). This review analyzes computational methods and highlights AI
Area of Science:
- Bioinformatics
- Evolutionary Biology
- Computational Biology
Background:
- Horizontal Gene Transfer (HGT) drives prokaryotic evolution and is linked to antimicrobial resistance (AMR).
- Numerous computational methods exist for HGT detection, but the role of Artificial Intelligence (AI) remains underexplored.
- Antimicrobial resistance (AMR) poses a significant global public health threat, making HGT detection crucial.
Purpose of the Study:
- To review current computational approaches for Horizontal Gene Transfer (HGT) detection.
- To investigate the emerging application of Artificial Intelligence (AI) in the field of HGT detection.
- To provide insights into the evolution of HGT detection methods and future research directions.
Main Methods:
- Systematic literature review of computational approaches for HGT detection.
- Categorization of existing methods into a hierarchical structure based on computational techniques.
- Analysis of trends, including the integration of AI-based methodologies.
Main Results:
- A growing interest in HGT detection is evident, with a recent surge in computational approaches, including AI-based methods.
- Existing computational methods for HGT detection were organized and their evolutionary progression was mapped.
- The review identified key challenges and opportunities for AI adoption in HGT detection.
Conclusions:
- AI presents a promising avenue for advancing HGT detection methodologies.
- Further research is needed to fully explore and optimize AI applications in understanding evolutionary dynamics and combating AMR.
- Addressing challenges in HGT detection, particularly AI integration, is vital for public health advancements.
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