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Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms

Authors Wan-Sheng Ke, Yuchi Hwang, Eugene Lin

Published 15 June 2010 Volume 2010:3 Pages 39—44

DOI https://doi.org/10.2147/AABC.S8656

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Peer reviewer comments 3

Wan-Sheng Ke1, Yuchi Hwang2, Eugene Lin2

1Department of Internal Medicine, Kuang Tien General Hospital, Taichung County, Taiwan; 2Vita Genomics, Inc., Jung-Shing Road, Wugu Shiang, Taipei, Taiwan

Abstract: Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics.

Keywords: chronic hepatitis C, artificial neural networks, interferon, pharmacogenomics, ribavirin, single nucleotide polymorphisms

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