Harnessing Interpretable Deep Learning to Predict Resistance in <i>Klebsiella pneumoniae</i>
Bibliographic record
Abstract
ABSTRACT Antimicrobial resistance constitutes an escalating global health threat, complicating therapeutic management and increasing morbidity and mortality. Deep learning approaches have emerged as promising tools for bacterial profiling based on omics data, particularly for predicting antimicrobial susceptibility from genomic information. This task relies on identifying genomic signatures associated with resistance mechanisms. Here, DeepMDC is introduced as a deep learning architecture designed for bacterial profiling using whole-genome data. Given that precise annotation at the gene or mutation level is often costly and ambiguous, phenotypic classification is formulated as a Multiple Instance Learning (MIL) problem, in which each genome is represented as a bag of instances with a single associated label. The core of DeepMDC is a Modern Hopfield Network that processes all open reading frames (ORFs), including small ones, derived from genomic data. A key feature of the architecture is its interpretability, enabled by attention mechanisms that facilitate biological insight and hypothesis generation. The model was evaluated against Klebsiella pneumoniae and four clinically relevant antibiotics (meropenem, cefepime, ceftazidime, and gentamicin), achieving strong performance in several metrics. Notably, genes associated with resistance consistently received high attention scores during inference, which validates the architecture and eventually may generate new hypotheses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".