Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

Abstract

Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic sequencing data often achieve varying results: high accuracy on some antibiotics but relatively low accuracy on others. Traditional machine learning (ML) approaches have been applied to classify drug resistance for MTB and have shown more stable performance. However, there is no study that uses deep learning architecture like Convolutional Neural Network (CNN) on a large and diverse cohort of MTB samples for AMR prediction. We developed 24 binary classifiers of MTB drug resistance status across eight anti-MTB drugs and three different ML algorithms: logistic regression, random forest and 1D CNN using a training dataset of 10,575 MTB isolates collected from 16 countries across six continents, where an extended pan-genome reference was used for detecting genetic features. Our 1D CNN architecture was designed to integrate both sequential and non-sequential features. In terms of F1-scores, 1D CNN models are our best classifiers that are also more accurate and stable than the state-of-the-art rule-based tool Mykrobe predictor (81.1 to 93.8%, 93.7 to 96.2%, 93.1 to 94.8%, 95.9 to 97.2% and 97.1 to 98.2% for ethambutol, rifampicin, pyrazinamide, isoniazid and ofloxacin respectively). We applied filter-based feature selection to find AMR relevant features. All selected variant features are AMR-related ones in CARD database. 78.8% of them are also in the catalogue of MTB mutations that were recently identified as drug resistance-associated ones by WHO. To facilitate ML model development for AMR prediction, we packaged every step into an automated pipeline and shared the source code at https://github.com/KuangXY3/MTB-AMR-classification-CNN.

Quantitative drug susceptibility testing for M. tuberculosis using unassembled sequencing data and machine learning

“…prior to mapping or assembly) were used as features. A total of 1.9 x 10 9 individual k -mers were considered. Where <5 k -mers were identified for an isolate, these were considered sequencing errors (Figure S4).…”

The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis

“…In particular, GenTB-RF reached the highest prediction for RIF [AUC 96% (95% CI 95–96%)]. Based on 1D CNN, using large and diverse M. tuberculosis isolates from six continents to verify the accuracy and steadiness of deep learning, another study developed a model which outperformed the advanced Mykrobe classifier which utilizes a De Bruijn graph to identify resistance profiles in antimicrobial-resistant prediction with higher F1 scores ( 75 ). Concurrently, it is worth mentioning that an innovative hierarchical attentive neural network has been constructed to predict the drug resistance of M. tuberculosis through genome-wide variants recently, discovering a potential gene related to drug resistance besides achieving supernal AUC and sensitivity in resistance recognition ( 76 ).…”

Explainable Deep Learning Approach for Multilabel Classification of Antimicrobial Resistance With Missing Labels

“…Even though this method internally handles missing labels, this approach cannot be applied to deep learning approaches. A few studies predicting AMR using deep learning approaches have been published [26]. However, missing label scenarios were not considered in the published approaches.…”

“…As part of this study, a few models were explored based on similar work done in the literature [14], [26]. Those models were then modified to suit this study through empirical studies.…”