A self-attention-based deep learning model for identifying key genes in insect pupal metamorphosis

Metamorphosis is the major innovation in the evolution of insect, playing an important role in environmental adaptation and biodiversity formation. However, it has been a challenge to identify the key genes in insect pupal metamorphosis. In this study, we constructed a library of word embeddings based on Word2vec for 293 insect protein sequences from 14 orders. A gene network classification model (GNCM) based on deep learning (DL) and self-attention mechanisms (SAM) was designed to identify key genes by calculating their importance (weights) in the metamorphosis of insect pupae. Empirical studies demonstrated that GNCM achieved a significantly better performance than other algorithms, including ANN, SVM, XGBoost, BiGRU, and BiLSTM, classification accuracy and interpretability. The results showed that GNCM identified 1,048 high-weight gene families, and differential expression analysis revealed that high-weight genes exhibited significantly higher expression levels than low-weight genes during the pupal stage. KEGG annotation showed that these genes were involved in functions with developmental, apoptosis, and immunity, which are crucial for insect metamorphosis. This study not only develops a novel artificial intelligence approach applicable to the identification of key genes, but also provided new insights for understanding the development of insect metamorphosis.