A new alignment-free method: Subsequence Correlation Coefficient Vector(SCCFV) for influenza A comparison using virus genomes

Abstract

Influenza viruses remain a formidable threat to global public health due to their high mutability and infectivity. Accurate prediction of influenza virus subtypes is crucial for clinical treatment and disease prevention. In recent years, machine learning methods have played an important role in studying influenza viruses. This study proposes a new alignment-free method based on the correlation of k-grams called Subsequence Correlation Coefficient Vector (SCCFV) to subtype hemagglutinin (HA) and neuraminidase (NA) of influenza virus. In the method, each influenza virus sequence is converted to four time series and the correlation coefficients of time series are utilized to extract the features of sequences. Then the supervised learning methods are used for the subtype classification of influenza viruses. We compare the effectiveness of the random forest, decision tree and support vector machine classifiers. Experimental results show that the random forest method achieves the best performance with an accuracy of 0.99979, an precision of 0.99996 and a recall of 0.99997. All prediction indicators of our method are significantly higher than traditional methods.