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Antibiotics
Special Issues
Machine Learning for Antimicrobial Resistance Prediction, 2nd Edition
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Machine Learning for Antimicrobial Resistance Prediction, 2nd Edition

A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Mechanism and Evolution of Antibiotic Resistance".

Deadline for manuscript submissions: 10 January 2026 | Viewed by 2697

Special Issue Editor

Dr. Asad Mustafa Karim

E-Mail Website
Guest Editor
Department of Biotechnology, Kyung Hee University, Seoul, Republic of Korea
Interests: antibiotic resistance; machine learning; infectious diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We published the first edition of the Special Issue "Machine Learning for Antimicrobial Resistance Prediction”, which was a success, and this has encouraged us to open a second edition focused on the same topic.

Antimicrobial resistance (AMR) is a major threat to global health and development that affects millions of people each year. The application of machine learning approaches to better understand and predict antimicrobial resistance will help to improve patients’ outcomes. A great deal of the research also continues to predict the resistance profiles of different bacteria species that cause human and animal infections. This Special Issue seeks manuscript submissions that further our understanding of antimicrobial resistance predictions in pathogenic bacteria. Submissions on resistance prediction, MIC profile prediction, the prediction of resistance sequences, resistance prediction in the environment, AMR gene prediction, and the prediction of AMR based on whole-genome sequencing are especially encouraged.

As a continuation of the Special Issues, the second edition will welcome manuscripts that consider the following requirements: 

  1. To employ machine learning or AI for prediction studies, AI should be used for prediction on experiment-based datasets.
  2. Authors can gather the data (such as MIC and resistance data) from online databases, and subsequently use AI for prediction studies.
  3. To ensure the transparency and reproducibility of the results presented in the study, authors are advised to add a fully executable and reproducible online code in the manuscript.

Additional point:
Manuscripts describing the use of computational modelling and/or molecular docking programs to predict the structures or activity of new antibiotics will not be considered, unless they present additional supporting data, such as biological test results (using microorganisms and/or pure protein).

Dr. Asad Mustafa Karim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • antimicrobial resistance prediction
  • artificial intelligence
  • machine learning
  • infectious diseases

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Related Special Issue

Published Papers (1 paper)

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Research

22 pages, 1636 KiB  
Article
Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions
by Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs and Georgios Feretzakis
Antibiotics 2024, 13(11), 1052; https://doi.org/10.3390/antibiotics13111052 - 6 Nov 2024
Cited by 5 | Viewed by 2298
Abstract
Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of [...] Read more.
Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of effective interventions. This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. It aims to determine the patterns from AMR gene data that are clinically relevant and, in public health, capable of informing strategies. Methods: We analyzed AMR gene data in the PanRes dataset by applying unsupervised learning techniques, namely K-means clustering and Principal Component Analysis (PCA). These techniques were applied to identify clusters based on gene length and distribution according to resistance class, offering insights into the resistance genes’ structural and functional properties. Data preprocessing, such as filtering and normalization, was conducted prior to applying machine learning methods to ensure consistency and accuracy. Our methodology included the preprocessing of data and reduction of dimensionality to ensure that our models were both accurate and interpretable. Results: The unsupervised learning models highlighted distinct clusters of AMR genes, with significant patterns in gene length, including their associated resistance classes. Further dimensionality reduction by PCA allows for clearer visualizations of relationships among gene groupings. These patterns provide novel insights into the potential mechanisms of resistance, particularly the role of gene length in different resistance pathways. Conclusions: This study demonstrates the potential of ML, specifically unsupervised approaches, to enhance the understanding of AMR. The identified patterns in resistance genes could support clinical decision-making and inform public health interventions. However, challenges remain, particularly in integrating genomic data and ensuring model interpretability. Further research is needed to advance ML applications in AMR prediction and management. Full article
(This article belongs to the Special Issue Machine Learning for Antimicrobial Resistance Prediction, 2nd Edition)
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