The COVID-19 outbreak highlighted the importance of mRNA vaccines; however, the thermal instability of mRNAs poses many challenges for vaccine production, storage, and transport, and accurately predicting their degradation is critical to safeguarding vaccine quality and efficacy. Traditional prediction methods always have the disadvantages of long experimental periods, excessive errors and unstable biological environments. Although machine learning and deep learning approaches can compensate for the shortcomings of traditional methods, using only one of these models to predict COVID-19 mRNA vaccine degradation is not effective. So, we propose a fusion model GGTC of GRU, GNN, Transformer and CNN. The results show that our fusion of GRU, CNN, Transformer and GNN models not only improves the accuracy of model prediction, but also improves the generalisation ability of the model.

Nana Guo, Junxi Li, Xin Guo. Application and evaluation of deep neural network fusion architecture in predicting COVID-19 mRNA vaccine degradation. Academic Journal of Medicine & Health Sciences (2025), Vol. 6, Issue 1: 15-22. https://doi.org/10.25236/AJMHS.2025.060103.

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