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With moderate masking, AttnGen achieves a validation accuracy of 96.73%, outperforming a conventional CNN baseline with 95.83% accuracy, while also exhibiting faster convergence and improv","title":"AttnGen: Attention-Guided Saliency Learning for Interpretable Genomic Sequence Classification","url":"https://arxiv.org/abs/2605.14073","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.14073v1 Announce Type: cross \nAbstract: Deep neural networks have achieved strong performance in genomic sequence classification; however, relating their predictions to biologically meaningful sequence patterns remains challenging. In this work, we present AttnGen, an attention-guided training framework that embeds interpretability directly into the optimization process. AttnGen computes nucleotide-level importance scores using an attention mechanism and progressively suppresses low-contribution positions during training. 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