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Antibiotics
Special Issues
Machine Learning for Antimicrobial Resistance Prediction
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Machine Learning for Antimicrobial Resistance Prediction

A special issue of Antibiotics (ISSN 2079-6382).

Deadline for manuscript submissions: closed (10 August 2024) | Viewed by 32212

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,

Antimicrobial resistance (AMR) is a major threat to global health and development that affects millions of people each year. In October 2020, the WHO declared the top ten global public health threats faced by humankind, and AMR was stated to be one of them. It is estimated that AMR could cause 10 million deaths each year by 2050 and force up to 24 million people into extreme poverty. Widespread and higher levels of resistance in bacteria have compromised the management and control of bacterial infections. Simultaneously, with the growing prevalence of bacterial resistance against antimicrobials, there has been a consistent reduction in the discovery of novel antibiotics. More concerningly, the antibiotic pipeline has slowed to a trickle. Although scientists have paid more attention to AMR, the overall situation is increasingly deteriorating. Many bacterial infections are treated empirically, and doctors prescribe a standard antibiotic to treat patients. There is a growing interest in knowing the antibiotic resistance profile before patient treatment begins. Since minimizing the time to optimal antimicrobial therapy significantly improves patient outcomes, rapid machine learning approaches for the prediction of resistance may have clinical utility. 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.

We sincerely suggest that manuscripts 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.

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 (6 papers)

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Research

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14 pages, 1678 KiB  
Article
Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples
by Lukas Lüftinger, Peter Májek, Thomas Rattei and Stephan Beisken
Antibiotics 2023, 12(2), 366; https://doi.org/10.3390/antibiotics12020366 - 9 Feb 2023
Cited by 2 | Viewed by 2317
Abstract
Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data [...] Read more.
Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples. Full article
(This article belongs to the Special Issue Machine Learning for Antimicrobial Resistance Prediction)
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12 pages, 3693 KiB  
Article
Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics
by Yunxiao Ren, Trinad Chakraborty, Swapnil Doijad, Linda Falgenhauer, Jane Falgenhauer, Alexander Goesmann, Oliver Schwengers and Dominik Heider
Antibiotics 2022, 11(11), 1611; https://doi.org/10.3390/antibiotics11111611 - 12 Nov 2022
Cited by 9 | Viewed by 3433
Abstract
Antimicrobial resistance (AMR) has become one of the serious global health problems, threatening the effective treatment of a growing number of infections. Machine learning and deep learning show great potential in rapid and accurate AMR predictions. However, a large number of samples for [...] Read more.
Antimicrobial resistance (AMR) has become one of the serious global health problems, threatening the effective treatment of a growing number of infections. Machine learning and deep learning show great potential in rapid and accurate AMR predictions. However, a large number of samples for the training of these models is essential. In particular, for novel antibiotics, limited training samples and data imbalance hinder the models’ generalization performance and overall accuracy. We propose a deep transfer learning model that can improve model performance for AMR prediction on small, imbalanced datasets. As our approach relies on transfer learning and secondary mutations, it is also applicable to novel antibiotics and emerging resistances in the future and enables quick diagnostics and personalized treatments. Full article
(This article belongs to the Special Issue Machine Learning for Antimicrobial Resistance Prediction)
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9 pages, 1362 KiB  
Article
Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance
by Muhammad Yasir, Asad Mustafa Karim, Sumera Kausar Malik, Amal A. Bajaffer and Esam I. Azhar
Antibiotics 2022, 11(11), 1593; https://doi.org/10.3390/antibiotics11111593 - 10 Nov 2022
Cited by 15 | Viewed by 3327
Abstract
Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this [...] Read more.
Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of genome sequence. In this study, we used transcriptomes of 410 multidrug-resistant isolates of Pseudomonas aeruginosa. We trained 10 machine learning (ML) classifiers on the basis of data on gene expression (GEXP) information and generated predictive models for meropenem, ciprofloxacin, and ceftazidime drugs. Among all the used ML models, four models showed high F1-score, accuracy, precision, and specificity compared with the other models. However, RandomForestClassifier showed a moderate F1-score (0.6), precision (0.61), and specificity (0.625) for ciprofloxacin. In the case of ceftazidime, RidgeClassifier performed well and showed F1-score (0.652), precision (0.654), and specificity (0.652) values. For meropenem, KNeighborsClassifier exhibited moderate F1-score (0.629), precision (0.629), and specificity (0.629). Among these three antibiotics, GEXP data on meropenem and ceftazidime improved diagnostic performance. The findings will pave the way for the establishment of a resistance profiling tool that can predict AMR on the basis of transcriptomic markers. Full article
(This article belongs to the Special Issue Machine Learning for Antimicrobial Resistance Prediction)
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Review

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18 pages, 1558 KiB  
Review
Silicon versus Superbug: Assessing Machine Learning’s Role in the Fight against Antimicrobial Resistance
by Tallon Coxe and Rajeev K. Azad
Antibiotics 2023, 12(11), 1604; https://doi.org/10.3390/antibiotics12111604 - 8 Nov 2023
Cited by 2 | Viewed by 2399
Abstract
In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and [...] Read more.
In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance. Full article
(This article belongs to the Special Issue Machine Learning for Antimicrobial Resistance Prediction)
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16 pages, 3132 KiB  
Review
Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation
by Tabish Ali, Sarfaraz Ahmed and Muhammad Aslam
Antibiotics 2023, 12(3), 523; https://doi.org/10.3390/antibiotics12030523 - 6 Mar 2023
Cited by 33 | Viewed by 11053
Abstract
Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial [...] Read more.
Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease. Full article
(This article belongs to the Special Issue Machine Learning for Antimicrobial Resistance Prediction)
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18 pages, 448 KiB  
Review
Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review
by Aikaterini Sakagianni, Christina Koufopoulou, Georgios Feretzakis, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs and Georgios Fildisis
Antibiotics 2023, 12(3), 452; https://doi.org/10.3390/antibiotics12030452 - 24 Feb 2023
Cited by 41 | Viewed by 7656
Abstract
Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as [...] Read more.
Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician’s point of view. Full article
(This article belongs to the Special Issue Machine Learning for Antimicrobial Resistance Prediction)
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