ACmix-Swin Deep Learning of 4-Day-Old Apis mellifera Larval Transcriptomes Reveals Early Caste-Biased Regulatory Hubs.

ABSTRACT:

Background/objectives

Early larval development is critical for caste and sex differentiation in honeybees. This study investigates molecular divergence in 4-day-old Apis mellifera larvae and introduces a customized deep learning model for hub-gene discovery.

Methods

Genome-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.

Results

Significant 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 reg

SUBMITTER: Gong P 

PROVIDER: S-EPMC12841262 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature