Prediction of mutations in H1 neuraminidases from North America influenza A virus engineered by internal randomness.

Recently, we defined the randomness within a protein as an important force engineering mutations. Thereafter we build a cause-mutation relationship, where one side is the quantified randomness and the other side is the occurrence or non-occurrence of mutation. This way, we switch the prediction of mutation into the problem of classification, which can be solved using either logistic regression or neural network. Very recently, we attempted to apply the logistic regression to predicting the mutation positions in proteins from influenza A virus. In this study, we attempt to explore the possibility of applying the neural network to predicting the mutation positions in H1 neuraminidase from influenza A virus. Then we applied the amino-acid mutating probability to predicting the would-be-mutated amino acids at predicted positions. The results confirm the possibility of prediction of mutation using this approach and pave the way for future development.