Work

Dengue virus serotype classification from complete genomes using a Transformer with chunk-based sequence modeling and explainability analysis

Year: 2026

Type: article

Abstract: Artificial intelligence is increasingly used for genomic surveillance of infectious diseases, but predictive models must be evaluated under realistic generalization settings and interpreted cautiously. We present XAI-DENFormer, an explainability-oriented framework for dengue virus (DENV) serotype classification from complete viral genomes. A curated dataset of 29,091 publicly available DENV genomes was encoded as nucleotide-level one-hot sequences and processed with a chunk-based Transformer encoder designed for memory-efficient whole-genome analysis. Generalization was assessed using three complementary validation protocols: CD-HIT cluster-aware, geographical, and temporal validation. DENFormer variants were benchmarked against Logistic Regression, a feed-forward neural network, Longformer, and Performer under identical conditions. The mean-pooling DENFormer configuration maintained high performance across all three validation protocols, with accuracies of 98.68–99.91% across CD-HIT folds, 97.18–100.00% across geographical holdouts, and 96.35–99.86% across temporal holdouts. To inspect model behavior, we combined attention maps, Gradient × Input attributions, embedding visualizations, and regional statistical analyses. The resulting profiles supported distributed serotype-associated signals across structural, non-structural, and untranslated regions rather than reliance on a single genomic locus. XAI-DENFormer provides a robust and interpretable framework for large-scale DENV genome classification and model-guided genomic inspection.

Source: Scientific Reports

DOI: 10.1038/s41598-026-69896-3