New AI network reads tuberculosis genomes and audits its own evidence for drug resistance

Researchers at the National School of Artificial Intelligence in Algiers have built a deep learning system that predicts drug resistance in Mycobacterium tuberculosis while simultaneously judging how trustworthy the sequencing data behind each prediction really is. The architecture, called the Interpretable Variant-Aware Multi-Path Network, or VAMP-Net, is described in the journal Applied Intelligence and was evaluated on four critical anti-tuberculosis drugs: rifampicin, rifabutin, ethambutol and isoniazid. Across the two rifamycin-class antibiotics the model achieved accuracies above 95 percent and areas under the receiver operating characteristic curve of roughly 0.97, significantly outperforming baseline convolutional and multilayer perceptron models.

The central problem the team set out to solve is twofold. First, resistance in the tuberculosis bacillus is not determined by single mutations acting alone; variants interact epistatically, meaning the effect of one mutation can depend on the presence of others elsewhere in the genome. Second, whole-genome sequencing pipelines vary in quality, and a variant call that looks convincing in one isolate may be technically noisy in another. Conventional machine learning classifiers typically consume a fixed vector of variant features and ignore both the set-like nature of genomic variation and the quality metrics attached to each call in the variant call format, or VCF.

VAMP-Net answers these challenges with a dual-pathway design. Path-1 employs a Set Attention Transformer to model the genome’s variants as a permutation-invariant set. Because a bacterial genome carries a variable number of mutations and there is no biologically meaningful ordering among them, the network must produce the same output regardless of the order in which variants are presented. Set attention layers, originally introduced in the Set Transformer framework, achieve this by exchanging information among set elements through attention blocks, allowing the model to capture epistatic dependencies between variant pairs without imposing an artificial sequence structure.

Path-2 takes a different slice of the evidence: the quality metrics embedded in the VCF, such as depth, mapping quality and the fraction of supporting reads. A one-dimensional convolutional neural network processes these metrics to produce an adaptive confidence score for each variant. The two pathways are then fused so that the final classification reflects not only which variants a genome carries but how reliable the calls are. The authors characterize the result as a learned integrated audit: the model does not simply trust raw sequencing depth, which can be inflated by repetitive regions or contamination, but learns to prioritize the fraction of supporting reads and relative confidence over raw depth as a way of mitigating technical noise.

The training data came from a public compendium associating more than 12,000 tuberculosis isolates with quantitative resistance phenotypes, and the complete dataset is openly available through the European Bioinformatics Institute. The code is also public, hosted on GitHub, which matters for the auditable character the authors emphasize. In clinical genomics, a model whose decision process cannot be inspected is difficult to deploy, because clinicians and laboratories need to know whether a resistance call rests on a canonical resistance mutation or on a sequencing artifact.

Interpretability was tested in two layers. The first layer uses Integrated Gradients, a feature attribution method that assigns each input feature an importance score by integrating gradients along a path from a baseline input to the actual input. Applied to VAMP-Net, the attribution analysis successfully recovered the canonical resistance targets: rpoB for the rifamycins, embB for ethambutol and katG for isoniazid. These genes are the well-established determinants that the World Health Organization’s technical guide on next-generation sequencing for resistance detection is built around, so recovering them is a strong sanity check that the model learned genuine biology rather than shortcuts.

The second layer of interpretability is more scientifically provocative. The attribution analysis also discovered high-impact novel loci that are not part of the canonical resistance catalogue. Functional enrichment analysis confirmed that these novel variants are not randomly scattered across the genome but constitute coherent metabolic modules, with a statistical significance of p equal to 0.00239, centered on cell-wall remodeling. This finding is biologically plausible: the tuberculosis cell wall is rich in mycolic acids and related lipids, and prior literature has documented that rifampicin-resistant rpoB mutants upregulate the phthiocerol dimycocerosate biosynthetic pathway, and that cross-resistance between clofazimine and bedaquiline arises through upregulation of the mmpL5 efflux transporter. The enriched modules suggest that resistance in the bacillus is embedded in compensatory metabolic rewiring rather than in isolated target mutations.

The paper’s appendix consolidates this mechanistic picture with systematic epistatic interaction tables. For each drug, the authors list the top variant pairs extracted by the attention mechanism, mapping every genomic coordinate against the H37Rv reference annotation and, for canonical loci, against the literature underlying the WHO catalogue. The tables highlight pairs involving efflux transporters such as mmpL5 and mmpL4, the fluoroquinolone target gyrA at a non-QRDR background position, the ethambutol-operon arabinosyltransferase embC, and the PhoPR two-component regulator phoR, which controls complex-lipid biosynthesis and stress response. An independent, model-agnostic statistical epistasis screen largely disjoint from the primary resistance-determinant loci corroborated the strongest pairs, ranking them by interaction significance and reporting odds ratios for carrying both variants versus neither.

Systematic ablation of the quality-aware pathway demonstrated that removing it degrades performance, confirming that the confidence-scoring branch contributes information beyond the variant set alone. The authors frame this as establishing a new paradigm for robust, auditable and clinically actionable resistance prediction, one that bridges genomic pathogenicity with technical reliability. In practice, this means a laboratory could inspect not only which loci drove a resistance call but also how the model weighted the sequencing evidence, making the classification traceable from mutation to metric to decision.

The work positions VAMP-Net as a tool for both diagnostic classification and mechanistic discovery in clinical genomics. Tuberculosis remains one of the world’s leading infectious killers, and drug resistance complicates treatment decisions that must often be made quickly. Whole-genome sequencing is increasingly used to guide therapy, but machine learning classifiers in this domain have been criticized for limited interpretability and for ignoring sequencing quality. By combining permutation-invariant set attention over variants with an explicit quality audit, and by validating its learned features against both canonical targets and independent statistical screens, the Algiers team has produced a system whose predictions come with a readable trail of evidence. Whether the discovered cell-wall remodeling modules translate into new biological understanding of how the bacillus tolerates antibiotics will require experimental follow-up, but the framework demonstrates that a single network can be simultaneously accurate, transparent and technically self-critical.

Subject of Research: Machine learning

Article Title: VAMP-Net: a variant-quality-aware, interpretable permutation-invariant multi-path network for auditable MTB drug-resistance

Article References: Boutorh, A., Baghdadi, K. H., & Daoud, A. (2026). VAMP-Net: a variant-quality-aware, interpretable permutation-invariant multi-path network for auditable MTB drug-resistance. Applied Intelligence, 56(16), Article 508. https://doi.org/10.1007/s10489-026-07496-4

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07496-4

Keywords: tuberculosis, drug resistance, deep learning, genomics, interpretable AI, VAMP-Net, machine learning, antimicrobial resistance, variant-quality-aware, interpretable, permutation-invariant, multi-path