Machine learning methods to identify markers and predict antimicrobial resistance in <i>Escherichia coli</i>
Bibliographic record
Abstract
Antimicrobial resistant strains of pathogenic Escherichia coli are a burden on the healthcare system, causing longer hospital stays and increased treatment costs compared to nonresistant strains. With whole genome sequencing almost ubiquitous in the analyses of outbreak and surveillance samples , in silico methods for feature identification can be faster and cheaper than traditional wet-lab methods. In this study, machine learning (ML) classification methods were used to predict antimicrobial resistance (AMR) and identify novel genomic markers of resistance. A total of 4300 E. coli whole genome sequences with laboratory-derived susceptible, intermediate, or resistant (SIR) data for 34 antimicrobials were collected. Three models—gradient boosted decision trees, support vector machines (SVMs), and artificial neural networks (ANNs)—were trained using genome subsequences ( k-mers) of length 11 to classify unknown isolates as SIR for each antimicrobial. The models achieved high average accuracies (93.6%, 92.7%, and 92.8%, respectively) for our dataset, outperforming database methods including AMRFinderPlus (63.9%) and ResFinder (75.7%). Tested on two smaller independent datasets, the models’ average accuracies were 81.6% (XGB), 79.9% (SVM), and 81.2% (ANN), while ResFinder’s average accuracy was 94.7%. An advantage of ML models over database methods is that they can identify novel markers of resistance, which is a key advantage for surveillance and research. As more genomic and AMR data become publicly available, these models are expected to further improve in performance and utility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".