Gene–gene and Gene–Environment Interactions in Interferon Therapy for Chronic Hepatitis C

Lin, Eugene; Hwang, Yuchi; Chen, Ellson Y.

doi:10.2217/14622416.8.10.1327

Cited by 28 publications

(27 citation statements)

References 37 publications

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“…4,5 The quality of the extracted genomic DNAs was checked by agarose gel electrophoresis analysis and stored at -80°C until use.…”

Section: Materials and Methods Patientsmentioning

confidence: 99%

“…4,5 More specifically, fragments of target genes were amplified by the PCR reaction. Amplification was carried out using 2700 PCR machines (ABI, Foster City, USA) and the amplified products were purified by membrane ultra-filtration with MultiScreen PCR plate (Millipore, Billerica, USA) according to the manufacturer's instructions.…”

Section: Materials and Methods Patientsmentioning

confidence: 99%

“…13 The rationale for selecting these SNPs is described in detail elsewhere. [3][4][5] The SNPs genetic markers of the participants were generated at the high-throughput genomics lab of Vita Genomics, Inc.…”

Section: Genetic Factorsmentioning

confidence: 99%

“…3,4 The efficacy of IFN-alfa/RBV is likely influenced by the combined effects of a number of genetic variants. [3][4][5] Accumulating evidence reveals that single nucleotide polymorphisms (SNPs) could be used as genetic markers to predict IFN-alfa/RBV treatment outcome in CHC. [3][4][5] Results of several studies [6][7][8] in different populations support the implication that the effects of IFN-alfa/RBV are associated with genetic variants.…”

mentioning

confidence: 99%

“…4,5 Moreover, the possible nonlinear relationships between genetic variants and antidepressant response have been explored using ANN algorithms in pharmacogenomics studies. 11,12 The previous researchers [3][4][5] mainly reported modeling IFN-alfa/RBV treatment response by using logistic regression or ANN methods without feature selection. In this work, we extended the previous research and applied both ANN algorithms and logistic regression with feature selection to predict IFN-alfa/RBV treatment outcomes using genetic factors.…”

mentioning

confidence: 99%

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

Hwang²,

Lin³

2010

AABC

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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.

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“…4,5 The quality of the extracted genomic DNAs was checked by agarose gel electrophoresis analysis and stored at -80°C until use.…”

Section: Materials and Methods Patientsmentioning

confidence: 99%

Section: Materials and Methods Patientsmentioning

confidence: 99%