Using decision tree learning to predict the responsiveness of hepatitis C patients to drug treatment
Open Access
- 1 January 2012
- journal article
- research article
- Published by Wiley in FEBS Open Bio
- Vol. 2 (1), 98-102
- https://doi.org/10.1016/j.fob.2012.04.007
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.Keywords
This publication has 13 references indexed in Scilit:
- IL28B SNP rs12979860 Is a Critical Predictor for On-Treatment and Sustained Virologic Response in Patients with Hepatitis C Virus Genotype-1 InfectionPLOS ONE, 2011
- ITPA gene variants protect against anaemia in patients treated for chronic hepatitis CNature, 2010
- Genetic variation in IL28B and spontaneous clearance of hepatitis C virusNature, 2009
- Genome-wide association of IL28B with response to pegylated interferon-α and ribavirin therapy for chronic hepatitis CNature Genetics, 2009
- IL28B is associated with response to chronic hepatitis C interferon-α and ribavirin therapyNature Genetics, 2009
- Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearanceNature, 2009
- The global burden of hepatitis CLiver International, 2009
- Peginterferon-α2a and Ribavirin Combination Therapy in Chronic Hepatitis CAnnals of Internal Medicine, 2004
- Side effects of therapy of hepatitis C and their managementHepatology, 2002
- Induction of decision treesMachine Learning, 1986