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Updated: Jan 29, 2026

Neuropharmacological Manipulation of Restrained and Free-flying Honey Bees, Apis mellifera
Published on: November 26, 2016
ACmix-Swin Deep Learning of 4-Day-Old Apis mellifera Larval Transcriptomes Reveals Early Caste-Biased Regulatory Hubs
Peixun Gong1, Jinyou Li2, Weixue Tian1
1College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
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 regulators, including Vg, LOC725841, LOC412768, and LOC100576841. qPCR confirmed RNA-seq trends.
Conclusions:
Caste- 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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