title: Using decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment.
abstract: The recommended treatment for patients with chronic hepatitis C, pegylated interferon α (PEG-IFN-α) plus rebavirin (RBV), does not provide a sustained virologic response in all patients, especially those with hepatitis C virus (HCV) genotype 1. It is therefore important to predict whether or not a new patient with HCV genotype 1 will be cured by the recommended treatment. We propose a prediction method for a new patient using a decision tree learning model based on SNPs evaluated in a genome-wide association study. By the decision tree learning for 142 Japanese patients with HCV genotype 1 (78 with null virologic response and 64 with virologic response), we can predict with high probability (93%) whether or not a new patient with HCV will be helped by the recommended treatment.
authors: The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, 1-7-1 Konodai, Ichikawa, Chiba 272-8516, Japan.
authors: Yoshihiro
authors: Y
authors: Kawamura
authors: Shigeru
authors: S
authors: Takasaki
authors: Masashi
authors: M
authors: Mizokami
keywords: Chronic hepatitis C genotype 1; Decision tree learning; GDI, Gini diversity index; GWAS, genome-wide association study; Genome-wide association study; HCV, hepatitis C virus; Het, one major and one minor genotype; MM, both major genotypes; NVR, null virologic response; Null virologic response; OR, Odds ratio; PEG-IFN-α, pegylated interferon α; Pegylated interferon α; RBV, ribavirin; Rebavirin; SNPs, single nucleotide polymorphisms; SVR, sustained virologic response; Single nucleotide polymorphism; Sustained virologic response; mm, both minor genotypes
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