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

Advances and Applications in Bioinformatics and Chemistry

Volumn 3, Issue 1, 2010, Pages 39-44

  Ke, Wan Sheng ^a   Hwang, Yuchi ^b   Lin, Eugene ^b  

^a KUANG TIEN GENERAL HOSPITAL   (Taiwan)

^b VITA GENOMICS INC   (Taiwan)

Author keywords

Artificial neural networks; Chronic hepatitis C; Interferon; Pharmacogenomics; Ribavirin; Single nucleotide polymorphisms

Indexed keywords

HEPATITIS C VIRUS;

EID: 80051566554     PISSN: None     EISSN: 11786949     Source Type: Journal    
DOI: None     Document Type: Article

References (31)

1. Lo Re V 3rd, Kostman JR. Management of chronic hepatitis C. Postgrad Med J. 2005;81(956):376-382.
2. Modi AA, Liang TJ. Hepatitis C: a clinical review. Oral Dis. 2008;14(1): 10-14.
3. Hwang Y, Chen EY, Gu ZJ, et al. Genetic Predisposition of Responsiveness to Therapy for Chronic Hepatitis C. Pharmacogenomics. 2006; 7(5):697-709.
4. Lin E, Hwang Y, Wang SC, Gu ZJ, Chen EY. An artificial neural network approach to the drug efficacy of interferon treatments. Pharmacogenomics. 2006;7(7):1017-1024.
5. Lin E, Hwang Y, Chen EY. Gene-gene and gene-environment interactions in interferon therapy for chronic hepatitis C. Pharmacogenomics. 2007;8(10):1327-1335.
6. Hijikata M, Ohta Y, Mishiro S. Identification of a single nucleotide polymorphism in the MxA gene promoter (G/T at nt-88) correlated with the response of hepatitis C patients to interferon. Intervirology. 2000;43(2):124-127.
7. Yee LJ, Tang J, Gibson AW, Kimberly R, Van Leeuwen DJ, Kaslow RA. Interleukin 10 polymorphisms as predictors of sustained response in antiviral therapy for chronic hepatitis C infection. Hepatology. 2001;33(3):708-712.
8. Sugimoto Y, Kuzushita N, Takehara T, et al. A single nucleotide polymorphism of the low molecular mass polypeptide 7 gene influences the interferon response in patients with chronic hepatitis C. J Viral Hepat. 2002;9(5):377-384.
9. Kung SY, Hwang JN. Neural networks for intelligent multimedia processing. Proc IEEE. 1998;86:1244-1272.
10. Erb RJ. Introduction to backpropagation neural network computation. Pharm Res. 1993;10:165-170.
11. Serretti A, Smerald E. Neural network analysis in pharmacogenetics of mood disorders. BMC Medical Genetics. 2004;5:27.
12. Lin E, Chen PS, Lee IH, et al. Modeling short-term antidepressant responsiveness with artificial neural networks. Open Access Bioinformatics. 2010 In press.
13. Lin E, Hwang Y. A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy. Mol Diagn Ther. 2008;12(4):219-223.
14. Bishop CM. Neural Networks for Pattern Recognition. Oxford, UK: Clarendon Press; 1995.
15. Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA, USA: Morgan Kaufmann Publishers; 2005.
16. Rumelhart DE, Hinton GE, William RJ. Learning internal representation by error propagation. Parallel Distributed Processing: Explorations. In: The Micro-Structure of Cognition, vol.1, Foundations. Cambridge, MA: MIT Press; 1996.
17. Huang LC, Hsu SY, Lin E. A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data. J Transl Med. 2009;7(1):81.
18. Kohavi R, John GH. Wrappers for feature subset selection. Artificial Intelligence. 1997;97:273-324.
19. Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27:861-874.
20. Hewett R, Kijsanayothin P. Tumor classification ranking from microarray data. BMC Genomics. 2008;9 Suppl 2:S21.
21. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23:2507-2517.
22. Sargent DJ. Comparison of artificial neural networks with other statistical approaches. Cancer. 2001;91(8):1636-1642.
23. Eftekhar B, Mohammad K, Ardebili HE, Ghodsi M, Ketabchi E. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Medical Informatics and Decision Making. 2005;5:3.
24. Lin E, Hwang Y, Liang KH, Chen EY. Pattern-recognition techniques with haplotype analysis in pharmacogenomics. Pharmacogenomics. 2007;8(1):75-83.
25. White H. Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings. Neural Networks. 1990;3: 535-549.
26. Sontag ED. Feedback stabilization using two-hidden-layers nets. IEEE Trans Neural Networks. 1992;3(6):981-990.
27. Lin CT, Lee G. Neural Fuzzy Systems. Upper Saddle River, NJ: Prentice-Hall; 1996.
28. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223-1238.
29. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005; 330(7494):765.
30. Kuperman GJ, Bobb A, Payne TH, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007;14(1):29-40.
31. Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003;10:523-530.