Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences
This work addresses the need for fast and accurate viral host prediction to prevent outbreaks, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of predicting influenza virus host origins using hemagglutinin sequences by evaluating machine learning algorithms, finding that a 5-grams-transformer neural network achieved up to 99.54% AUCPR, 98.01% F1 score, and 96.60% MCC at higher classification levels.
Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has been increasing interest in using machine learning algorithms to provide fast and accurate predictions for viral sequences. In this study, real testing data sets and a variety of evaluation metrics were used to evaluate machine learning algorithms at different taxonomic levels. As hemagglutinin is the major protein in the immune response, only hemagglutinin sequences were used and represented by position-specific scoring matrix and word embedding. The results suggest that the 5-grams-transformer neural network is the most effective algorithm for predicting viral sequence origins, with approximately 99.54% AUCPR, 98.01% F1 score and 96.60% MCC at a higher classification level, and approximately 94.74% AUCPR, 87.41% F1 score and 80.79% MCC at a lower classification level.