About this Journal Submit a Manuscript Table of Contents
Journal of Biomedicine and Biotechnology
Volume 2011 (2011), Article ID 875309, 8 pages
http://dx.doi.org/10.1155/2011/875309
Research Article

Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

1Department of Mathematics, Southeast University, Nanjing 210096, China
2Department of Mathematics, China Pharmaceutical University, Nanjing 210009, China
3State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China

Received 28 November 2010; Revised 3 February 2011; Accepted 24 March 2011

Academic Editor: Sanford I. Bernstein

Copyright © 2011 Fang-Rong Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Timothy, A. Charalambos, and H. Phillip, “Non-invasive markers for the prediction of fibrosis in chronic hepatitis C infection,” Hepatology Research, vol. 38, no. 8, pp. 762–769, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. R. A. Standish, E. Cholongitas, A. Dhillon, A. K. Burroughs, and A. P. Dhillon, “An appraisal of the histopathological assessment of liver fibrosis,” Gut, vol. 55, no. 4, pp. 569–578, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. J. L. Dienstag, “The role of liver biopsy in chronic hepatitis C,” Hepatology, vol. 36, no. 5, pp. S152–S160, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Regev, M. Berho, L. J. Jeffers et al., “Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection,” American Journal of Gastroenterology, vol. 97, no. 10, pp. 2614–2618, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Kentaro, K. Naoto, and H. Senju, “Transient elastography: applications and limitations,” Hepatology Research, vol. 38, no. 11, pp. 1063–1068, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. S. C. Gordon, I. Jacobson, C. Hézode et al., “Evaluation of a panel of non-invasive serum markers to differentiate mild from moderate-to-advanced liver fibrosis in chronic hepatitis C patients,” Journal of Hepatology, vol. 41, no. 6, pp. 935–942, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. E. Giannini and R. Testa, “Noninvasive diagnosis of fibrosis: the truth is rarely pure and never simple,” Hepatology, vol. 38, no. 5, pp. 1312–1313, 2003. View at Scopus
  8. S. Naveau, B. Raynard, V. Ratziu et al., “Biomarkers for the prediction of liver fibrosis in patients with chronic alcoholic liver disease,” Clinical Gastroenterology and Hepatology, vol. 3, no. 2, pp. 167–174, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. E. Rossi, L. Adams, A. Prins et al., “Validation of the FibroTest biochemical markers score in assessing liver fibrosis in hepatitis C patients,” Clinical Chemistry, vol. 49, no. 3, pp. 450–454, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. D. M. Rocke and D. V. Nguyen, “On partial least squares dimension reduction for microarray-based classification: a simulation study,” Computational Statistics and Data Analysis, vol. 46, no. 3, pp. 407–425, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Li, C. Campbell, and M. Tipping, “Bayesian automatic relevance determination algorithms for classifying gene expression data,” Bioinformatics, vol. 18, no. 10, pp. 1332–1339, 2002. View at Scopus
  13. G. Debashis and M. C. Arul, “Classification and selection of biomarkers in genomic data using LASSO,” Journal of Biomedicine and Biotechnology, vol. 2005, no. 2, pp. 147–154, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. Z. Qiao, L. Zhou, and J. Z. Huang, “Sparse linear discriminant analysis with applications to high dimensional low sample size data,” IAENG International Journal of Applied Mathematics, vol. 39, no. 1, pp. 1–13, 2009. View at Scopus
  15. J. Fan and R. Li, “Variable selection via nonconcave penalized likelihood and its oracle properties,” Journal of the American Statistical Association, vol. 96, no. 456, pp. 1348–1360, 2001. View at Scopus
  16. Y. Kim, S. Kwon, and S. Heun Song, “Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data,” Computational Statistics and Data Analysis, vol. 51, no. 3, pp. 1643–1655, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. R. J. Tibshirani, “Regression coefficient and autoregressive order shrinkage and selection via the LASSO,” Journal of the Royal Statistical Society B, vol. 58, no. 1, pp. 267–288, 1996.
  18. R. Tibshirani, “The LASSO method for variable selection in the cox model,” Statistics in Medicine, vol. 16, no. 4, pp. 385–395, 1997.
  19. D. B. Rubin, “The bayesian bootstrap,” The Annals of Statistic, vol. 9, no. 1, pp. 130–134, 1981.
  20. J. Gu, S. Ghosal, and A. Roy, “Bayesian bootstrap estimation of ROC curve,” Statistics in Medicine, vol. 27, no. 26, pp. 5407–5420, 2008. View at Scopus
  21. Y. Imanishi, N. Maeda, K. Otogawa et al., “Herb medicine Inchin-ko-to (TJ-135) regulates PDGF-BB-dependent signaling pathways of hepatic stellate cells in primary culture and attenuates development of liver fibrosis induced by thioacetamide administration in rats,” Journal of Hepatology, vol. 41, no. 2, pp. 242–250, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Kuriyama, M. Yamazaki, A. Mitoro et al., “Hepatocellular carcinoma in an orthotopic mouse model metastasizes intrahepatically in cirrhotic but not in normal liver,” International Journal of Cancer, vol. 80, no. 3, pp. 471–476, 1999. View at Scopus
  23. P. Bedossa, D. Dargère, and V. Paradis, “Sampling variability of liver fibrosis in chronic hepatitis C,” Hepatology, vol. 38, no. 6, pp. 1449–1457, 2003. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Fan and H. Peng, “Nonconcave penalized likelihood with a diverging number of parameters,” Annals of Statistics, vol. 32, no. 3, pp. 928–961, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Efron, T. Hastie, I. Johnstone et al., “Least angle regression,” Annals of Statistics, vol. 32, no. 2, pp. 407–499, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. P. L. Flom and D. L. Cassell, “Stopping stepwise: why stepwise and similar selection methods are bad, and what use,” in Proceedings of the 20th Conference for the NorthEast SAS® Users Group (NESUG '07), 2007.
  27. F. H. Anderson, L. Zeng, N. R. Rock, and E. M. Yoshida, “An assessment of the clinical utility of serum ALT and AST in chronic hepatitis C,” Hepatology Research, vol. 18, no. 1, pp. 63–71, 2000. View at Publisher · View at Google Scholar · View at Scopus
  28. S. L. Friedman, D. C. Rockey, and D. M. Bissell, “Hepatic fibrosis 2006: report of the third AASLD single topic conference,” Hepatology, vol. 45, no. 1, pp. 242–249, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. F. E. Harrell, Regression Modeling Strategies: With Applications to Linear Mode to Logistic Regression, and Survival Analysis, Springe, New York, NY, USA, 2001.