Comparing the prediction performance of machine learning and deep learning methods for 3c-like protease cleavage sites.

The impact of COVID-19 on the human body after infection requires further investigation. The protease carried by the virus may interact with cellular proteins, thereby affecting the regulatory pathways. Machine learning can provide predictive information to aid the research design in such studies. This study examined the effectiveness of different encoding methods for training models using traditional machine learning and deep learning methods. Using the advantages of deep learning methods for high-dimensional data analysis, amino acid properties were extracted and classified. Viral polyprotein data were used as the training set and human protein substrates were collected from the literature as the independent test set. In the one-dimensional convolutional neural network model, the accuracy of the independent test set was the highest, at 0.832.