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

• URL: http://arxiv.org/abs/2512.21786v1
• Date: Thu, 25 Dec 2025 21:28:54 GMT
• Title: VAMP-Net: An Interpretable Multi-Path Framework of Genomic Permutation-Invariant Set Attention and Quality-Aware 1D-CNN for MTB Drug Resistance
• Abstract summary: We present a novel Interpretable Variant-Aware Multi-Path Network (VAMP-Net)<n>Path-1 employs a Set Attention Transformer processing permutation-invariant variant sets to capture epistatic interactions between genomic loci.<n>Path-2 utilizes a 1D Confabal Network that analyzes Variant Call Format quality metrics to learn confidence scores.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Genomic prediction of drug resistance in Mycobacterium tuberculosis remains challenging due to complex epistatic interactions and highly variable sequencing data quality. We present a novel Interpretable Variant-Aware Multi-Path Network (VAMP-Net) that addresses both challenges through complementary machine learning pathways. Path-1 employs a Set Attention Transformer processing permutation-invariant variant sets to capture epistatic interactions between genomic loci. Path-2 utilizes a 1D Convolutional Neural Network that analyzes Variant Call Format quality metrics to learn adaptive confidence scores. A fusion module combines both pathways for final resistance classification. We conduct comparative evaluations of unmasked versus padding-masked Set Attention Blocks, and demonstrate that our multi-path architecture achieves superior performance over baseline CNN and MLP models, with accuracy exceeding 95% and AUC around 97% for Rifampicin (RIF) and Rifabutin (RFB) resistance prediction. The framework provides dual-layer interpretability: Attention Weight Analysis reveals Epistatic networks, and Integrated Gradients (IG) was applied for critical resistance loci (notably rpoB), while gradient-based feature importance from the CNN pathway uncovers drug-specific dependencies on data quality metrics. This architecture advances clinical genomics by delivering state-of-the-art predictive performance alongside auditable interpretability at two distinct levels, genetic causality of mutation sets and technical confidence of sequencing evidence, establishing a new paradigm for robust, clinically-actionable resistance prediction.

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