Predicting drug resistance in <i>M. tuberculosis</i> using a Long-term Recurrent Convolutional Network
Bibliographic record
Abstract
ABSTRACT Motivation Drug resistance in Mycobacterium tuberculosis (MTB) is a growing threat to human health worldwide. One way to mitigate the risk of drug resistance is to enable clinicians to prescribe the right antibiotic drugs to each patient through methods that predict drug resistance in MTB using whole-genome sequencing (WGS) data. Existing machine learning methods for this task typically convert the WGS data from a given bacterial isolate into features corresponding to single-nucleotide polymorphisms (SNPs) or short sequence segments of a fixed length K ( K -mers). Here, we introduce a gene burden-based method for predicting drug resistance in TB. We define one numerical feature per gene corresponding to the number of mutations in that gene in a given isolate. This representation greatly reduces the number of model parameters. We further propose a model architecture that considers both gene order and locality structure through a Long-term Recurrent Convolutional Network (LRCN) architecture, which combines convolutional and recurrent layers. Results We find that using these strategies yields a substantial, statistically significant improvement over state-of-the-art methods on a large dataset of M. tuberculosis isolates, and suggest that this improvement is driven by our method’s ability to account for the order of the genes in the genome and their organization into operons. Availability The implementations of our feature preprocessing pipeline 1 and our LRCN model 2 are publicly available, as is our complete dataset 3 . Supplementary information Additional data are available in the Supplementary Materials document 4 .
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".