| Autor: |
Sakagianni A; Intensive Care Unit, Sismanogelio General Hospital, 37 Sismanogleiou Str., 15126 Marousi, Greece., Koufopoulou C; Anesthesiology Department, Aretaieio University Hospital, National and Kapodistrian University of Athens, Vass. Sofias 76, 11528 Athens, Greece., Koufopoulos P; Department of Internal Medicine, Sismanogleio General Hospital, 15126 Marousi, Greece., Kalantzi S; Department of Internal Medicine & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Athens, Greece., Theodorakis N; Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Athens, Greece., Nikolaou M; Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Athens, Greece., Paxinou E; School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece., Kalles D; School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece., Verykios VS; School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece., Myrianthefs P; Faculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, Greece., Feretzakis G; School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece. |
| Abstrakt: |
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. |
