Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms.

BACKGROUND: Hepatitis C virus (HCV) causes chronic hepatitis C in 2-3% of world population and remains one of the health threatening human viruses, worldwide. In the absence of an effective vaccine, therapeutic approach is the only option to combat hepatitis C. Interferon-alpha (IFN-alpha) and ribavirin (RBV) combination alone or in combination with recently introduced new direct-acting antivirals (DAA) is used to treat patients infected with HCV. The present study utilized feature selection methods (Gini Index, Chi Squared and machine learning algorithms) and other bioinformatics tools to identify genetic determinants of therapy outcome within the entire HCV nucleotide sequence.

RESULTS: Using combination of several algorithms, the present study performed a comprehensive bioinformatics analysis and identified several nucleotide attributes within the full-length nucleotide sequences of HCV subtypes 1a and 1b that correlated with treatment outcome. Feature selection algorithms identified several nucleotide features (e.g. count of hydrogen and CG). Combination of algorithms utilized the selected nucleotide attributes and predicted HCV subtypes 1a and 1b therapy responders from non-responders with an accuracy of 75.00% and 85.00%, respectively. In addition, therapy responders and relapsers were categorized with an accuracy of 82.50% and 84.17%, respectively. Based on the identified attributes, decision trees were induced to differentiate different therapy response groups.

CONCLUSIONS: The present study identified new genetic markers that potentially impact the outcome of hepatitis C treatment. In addition, the results suggest new viral genomic attributes that might influence the outcome of IFN-mediated immune response to HCV infection.