AI-driven prediction of mRNA vaccine degradation rates with dropout-enhanced hyperparameter optimisation

Rapid mRNA vaccine degradation necessitates accurate prediction to ensure efficacy and mitigate risks. Despite challenges, mRNA vaccines' affordability, high efficacy, and minimal side effects justify intensive research efforts. Multifaceted data analysis enhances prediction efficiency. This study leverages bioinformatics with label-encoding of tetra-nitrogenous bases (4-ntb-lbA) and advanced models, refined through extensive review. Hyperparameter optimisation is improved with the dropout-enhanced technique (DEet), addressing traditional shortcomings - frequently suggesting suboptimal configurations. Results demonstrate that 4-ntb-lbA and DEet offer practical solutions. Specifically, 4-ntb-lbA upholds interdisciplinarity and minimises overfitting, while DEet mitigates suboptimality and accelerates convergence. These improvements are notable in the three-layered-wrapped-stacked BiLSTM (3lw-BiLSTM) model with 0.125-DEet to Bayesian optimisation with Gaussian process (BOGP) on a medium-sized subset (MSbT) at epoch 150, where training and validation losses reached 0.0015 and 0.0018, respectively, significantly reducing computational costs. This interdisciplinary approach is valuable for biotechnology and biomedicine, underscoring its contribution to efficient data analysis.