COVID-19 mRNA Vaccine Degradation Prediction Using LR and LGBM Algorithms

Soon Hwai, Ing Abdullah Azian Azamimi Harun Nor Hazlyna Kanaya Shigehiko

The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvellous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19. Two machine learning algorithms, which are, Linear Regression (LR) and Light Gradient Boosting Machine (LGBM) are proposed for models development using Python language. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that LGBM (0.2447) performs better than LR (0.3957) for this study when evaluated with the RMSE metric.