Using decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment.
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ABSTRACT:
SUBMITTER: Kawamura Y
PROVIDER: S-EPMC3645974 | biostudies-literature | 2012
REPOSITORIES: biostudies-literature
Publications
Using decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment.
Kawamura Yoshihiro Y Takasaki Shigeru S Mizokami Masashi M
FEBS open bio 20120511
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-wid ...[more]
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