Open AccessUsing decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment
Author(s) -
Kawamura Yoshihiro,
Takasaki Shigeru,
Mizokami Masashi
Publication year - 2012
Publication title -
febs open bio
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.718
H-Index - 31
ISSN - 2211-5463
DOI - 10.1016/j.fob.2012.04.007
Subject(s) - medicine , pegylated interferon , decision tree , hepatitis c virus , genotype , hepatitis c , drug , chronic hepatitis , immunology , machine learning , virus , pharmacology , ribavirin , gene , biology , computer science , biochemistry
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.