Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions
<b>Background/Objectives:</b> 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...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Antibiotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6382/13/11/1052 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850149793431552000 |
|---|---|
| author | Aikaterini Sakagianni Christina Koufopoulou Petros Koufopoulos Sofia Kalantzi Nikolaos Theodorakis Maria Nikolaou Evgenia Paxinou Dimitris Kalles Vassilios S. Verykios Pavlos Myrianthefs Georgios Feretzakis |
| author_facet | Aikaterini Sakagianni Christina Koufopoulou Petros Koufopoulos Sofia Kalantzi Nikolaos Theodorakis Maria Nikolaou Evgenia Paxinou Dimitris Kalles Vassilios S. Verykios Pavlos Myrianthefs Georgios Feretzakis |
| author_sort | Aikaterini Sakagianni |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-8e8b675e83054b4896b6a09be3f4d474 |
| institution | OA Journals |
| issn | 2079-6382 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj-art-8e8b675e83054b4896b6a09be3f4d4742025-08-20T02:26:47ZengMDPI AGAntibiotics2079-63822024-11-011311105210.3390/antibiotics13111052Data-Driven Approaches in Antimicrobial Resistance: Machine Learning SolutionsAikaterini Sakagianni0Christina Koufopoulou1Petros Koufopoulos2Sofia Kalantzi3Nikolaos Theodorakis4Maria Nikolaou5Evgenia Paxinou6Dimitris Kalles7Vassilios S. Verykios8Pavlos Myrianthefs9Georgios Feretzakis10Intensive Care Unit, Sismanogelio General Hospital, 37 Sismanogleiou Str., 15126 Marousi, GreeceAnesthesiology Department, Aretaieio University Hospital, National and Kapodistrian University of Athens, Vass. Sofias 76, 11528 Athens, GreeceDepartment of Internal Medicine, Sismanogleio General Hospital, 15126 Marousi, GreeceDepartment of Internal Medicine & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Athens, GreeceDepartment of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Athens, GreeceDepartment of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Athens, GreeceSchool of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, GreeceSchool of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, GreeceSchool of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, GreeceFaculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, GreeceSchool of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece<b>Background/Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2079-6382/13/11/1052antimicrobial resistancemachine learningk-means clusteringprincipal component analysisgenomic data analysis |
| spellingShingle | Aikaterini Sakagianni Christina Koufopoulou Petros Koufopoulos Sofia Kalantzi Nikolaos Theodorakis Maria Nikolaou Evgenia Paxinou Dimitris Kalles Vassilios S. Verykios Pavlos Myrianthefs Georgios Feretzakis Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions Antibiotics antimicrobial resistance machine learning k-means clustering principal component analysis genomic data analysis |
| title | Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions |
| title_full | Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions |
| title_fullStr | Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions |
| title_full_unstemmed | Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions |
| title_short | Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions |
| title_sort | data driven approaches in antimicrobial resistance machine learning solutions |
| topic | antimicrobial resistance machine learning k-means clustering principal component analysis genomic data analysis |
| url | https://www.mdpi.com/2079-6382/13/11/1052 |
| work_keys_str_mv | AT aikaterinisakagianni datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT christinakoufopoulou datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT petroskoufopoulos datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT sofiakalantzi datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT nikolaostheodorakis datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT marianikolaou datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT evgeniapaxinou datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT dimitriskalles datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT vassiliossverykios datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT pavlosmyrianthefs datadrivenapproachesinantimicrobialresistancemachinelearningsolutions AT georgiosferetzakis datadrivenapproachesinantimicrobialresistancemachinelearningsolutions |