Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

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 ge...

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Bibliographic Details
Published in:Antibiotics (Basel) Vol. 11; no. 11; p. 1593
Main Authors: Yasir, Muhammad, Karim, Asad Mustafa, Malik, Sumera Kausar, Bajaffer, Amal A., Azhar, Esam I.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2022
MDPI
Subjects:
Accuracy
Algorithms
Antibiotics
Antimicrobial agents
Antimicrobial resistance
Bacterial infections
Ceftazidime
Ciprofloxacin
Comparative analysis
Data mining
Decision trees
Drug resistance
Drug resistance in microorganisms
Gene expression
Genomes
Genomics
Infections
Learning algorithms
Machine learning
Meropenem
Multidrug resistance
Multidrug resistant organisms
Nucleotide sequence
Pathogens
Patients
Performance evaluation
Physicians
Prediction models
Pseudomonas aeruginosa
Transcriptomes
Transcriptomics
Saudi Arabia
Taiwan
ISSN:2079-6382, 2079-6382
Online Access:Get full text
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Summary: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.
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These authors contributed equally to this work.
ISSN:2079-6382
2079-6382
DOI:10.3390/antibiotics11111593