Abstract
Background: Interferon-α (IFNα) in combination with ribavirin can be used for the treatment of patients with chronic hepatitis C. This therapeutic approach achieves an overall sustained response rate of approximately 40%, but treatment takes 6–12 months and patients often experience significant adverse reactions.
Objective: We aim to develop a tool to distinguish potential responders from nonresponders prior to initiation of IFNα-ribavirin treatment.
Methods: Using single nucleotide polymorphisms (SNPs) and viral genotype, we applied the support vector machine (SVM) algorithm to build a tool to predict responsiveness to IFNα-ribavirin combination therapy. Furthermore, we utilized the SVM algorithm with the recursive feature elimination method to identify a subset of factors that are significantly more influential than the others.
Results and conclusion: The SVM model is a promising method for inferring responsiveness to IFNα dealing with the complex nonlinear relationship between factors (such as SNPs and viral genotype) and successful therapy. In this study, we demonstrate that our tool may allow patients and doctors to make more informed decisions by analyzing host SNP and viral genotype information.
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Acknowledgments
The authors extend their sincere thanks to Vita Genomics, Inc. for funding this research and to Dr Pei-Jer Chen of the Hepatitis Research Center, National Taiwan University, Dr You-Chen Chao of the Tri-Service General Hospital, Dr Ming-Lung Yu of the Kaohsiung Medical University Hospital, and Dr Chuan-Mo Lee of the Kaohsiung Chang-Gung Memorial Hospital for research collaboration.
The authors would also like to thank Dr David Schlessinger for helpful suggestions and the anonymous reviewers for their constructive comments, which improved the context and presentation of this paper.
The authors have no conflicts of interest that are directly relevant to the content of this study.
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Lin, E., Hwang, Y. A Support Vector Machine Approach to Assess Drug Efficacy of Interferon-α and Ribavirin Combination Therapy. Mol Diag Ther 12, 219–223 (2008). https://doi.org/10.1007/BF03256287
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DOI: https://doi.org/10.1007/BF03256287