ArticleGenes2025
ACmix-Swin Deep Learning of 4-Day-Old
Article in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND/
objectivesEarly larval development is critical for caste and sex differentiation in honeybees. This study investigates molecular divergence in 4-day-old
methodsGenome-guided RNA-seq, DEGs, WGCNA, and splicing analyses were integrated. A hybrid convolution-attention model, ACmix-Swin, combined with WGAN-GP augmentation, was developed to classify larvae and prioritize caste-biased genes. Selected genes were validated by qPCR.
resultsSignificant caste- and sex-specific divergence was detected in cuticle formation, hormone metabolism, and reproductive signaling. ACmix-Swin achieved the highest accuracy among baseline models and consistently identified key regulators, including
conclusionsCaste- and sex-specific transcriptional programs are established early in larval development. The ACmix-Swin framework provides an effective strategy for high-dimensional transcriptome interpretation and robust hub-gene identification.
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Registered trials
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