VAMP-Net: An Interpretable Multi-Path Network of Genomic Permutation-Invariant Set Attention and Quality-Aware 1D-CNN for MTB Drug Resistance

TL;DR

VAMP-Net is an interpretable multi-path neural network that combines permutation-invariant set attention and quality-aware CNNs to improve genomic prediction of drug resistance in tuberculosis, with high accuracy and novel insights.

Contribution

The paper introduces VAMP-Net, a novel architecture integrating set attention and quality metrics for robust, interpretable drug resistance prediction in genomics.

Findings

VAMP-Net achieves over 95% accuracy and 0.97 AUC on anti-TB drug resistance prediction.

Feature attribution recovers known resistance loci and identifies novel impactful variants.

Systematic ablation shows the model's ability to prioritize reliable signals over noisy data.

Abstract

Genomic prediction of drug resistance in Mycobacterium tuberculosis is often hindered by complex epistatic interactions and variable sequencing quality. We present the Interpretable Variant-Aware Multi-Path Network (VAMP-Net), a novel architecture addressing these challenges through a dual-pathway approach. Path-1 utilizes a Set Attention Transformer to model permutation-invariant variant sets and capture epistatic dependencies, while Path-2 employs a 1D-CNN to analyze VCF quality metrics for adaptive confidence scoring. Evaluated on four critical anti-TB drugs, VAMP-Net significantly outperforms baseline CNN and MLP models, achieving accuracies > 95% and AUCs around 0.97 for Rifampicin and Rifabutin. Feature attribution analysis via Integrated Gradients successfully recovered canonical targets (rpoB, embB, katG) and discovered high-impact novel loci. Functional enrichment confirmed these novel variants constitute non-random metabolic modules (p=0.00239) centered on cell-wall remodeling. Furthermore, systematic ablation of the Quality-Aware pathway demonstrates that the model performs a learned "integrated audit," prioritizing the Fraction of Supporting Reads and relative confidence over raw depth to mitigate technical noise. This dual-layer interpretability, bridging genomic pathogenicity with technical reliability, establishes a new paradigm for robust, auditable, and clinically actionable resistance prediction, positioning VAMP-Net as an important tool for both diagnostic classification and mechanistic discovery in clinical genomics.