Akademik Veri Yönetim Sistemi
IEEE Transactions on Computational Biology and Bioinformatics, cilt.23, sa.1, ss.65-74, 2026 (SCI-Expanded, Scopus)
Antimicrobial resistance (AMR) is a global health problem that poses a threat for now and poses an even greater threat for the future. Since the discovery of the first antibiotics, pathogens have developed different mechanisms of resistance against antibiotics. Today, the technology to understand the mechanisms of AMR and their genomics is more competent than ever. This paper provides a wide range of information about using genomic data combined with machine learning for predicting antibiotic resistance and proposes a multi-model approach ASAP (Antibiotic Susceptibility and Antibiogram Prediction) for creating antibiograms. In this work, 10 different machine learning models, including Convolutional Neural Network, Nearest Neighbor, Random Forest, XGBoost, CatBoost, Naive Bayes, Support Vector Machines, Light Gradient Boosting Machine, Gradient Boost, and Logistic Regression, have been tested, evaluated, and compared for their predictive capabilities. For data preprocessing, different methods of feature extraction through n-gram encoding have been tested. For the evaluation of the models, accuracy, recall, precision, and F1 scores are used. Experiments show that models can predict the antibiotic resistance of a given pathogen sequence with up to 0.99 accuracy and 0.90+ macro average recall. The best performing model for this work has been XGBoost with 0.99 accuracy, and the least predictive model has been Naive Bayes with 0.89 accuracy. Our proposed method results shows that machine learning with gene sequences are promising tools to improve the current manual antibiogram creation, maintenance process and can provide healthcare professionals with valuable insights for less empirical antibiotic prescribing.