- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
International Journal of Endocrinology
Volume 2013 (2013), Article ID 850735, 8 pages
Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population
1Department of Biomedical Science and Environmental Biology, Graduate Institute of Natural Products, College of Pharmacy,
Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
2Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung 807, Taiwan
3Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan
Received 26 October 2012; Revised 21 December 2012; Accepted 27 December 2012
Academic Editor: Guang-Da Xiang
Copyright © 2013 Hsueh-Wei Chang 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.
- A. Klibanski, L. Adams-Campbell, T. Bassford et al., “Osteoporosis prevention, diagnosis, and therapy,” Journal of the American Medical Association, vol. 285, no. 6, pp. 785–795, 2001.
- P. Mezquita-Raya, M. Muñoz-Torres, G. Alonso et al., “Susceptibility for postmenopausal osteoporosis: interaction between genetic, hormonal and lifestyle factors,” Calcified Tissue International, vol. 75, no. 5, pp. 373–379, 2004.
- S. L. Hui, C. W. Slemenda, and C. C. Johnston, “Age and bone mass as predictors of fracture in a prospective study,” Journal of Clinical Investigation, vol. 81, no. 6, pp. 1804–1809, 1988.
- M. A. Osmanagaoglu, B. Okumuş, T. Osmanagaoglu, and H. Bozkaya, “The relationship between serum dehydroepiandrosterone sulfate concentration and bone mineral density, lipids, and hormone replacement therapy in premenopausal and postmenopausal women,” Journal of Women's Health, vol. 13, no. 9, pp. 993–999, 2004.
- M. Sadat-Ali, I. M. Al-Habdan, F. A. Al-Mulhim, and A. Y. El-Hassan, “Bone mineral density among postmenopausal Saudi women,” Saudi Medical Journal, vol. 25, no. 11, pp. 1623–1625, 2004.
- L. J. Melton, S. H. Kan, M. A. Frye, H. W. Wahner, W. M. O'Fallon, and B. L. Riggs, “Epidemiology of vertebral fractures in women,” American Journal of Epidemiology, vol. 129, no. 5, pp. 1000–1011, 1989.
- V. Camozzi, V. Carraro, M. Zangari, F. Fallo, F. Mantero, and G. Luisetto, “Use of quantitative ultrasound of the hand phalanges in the diagnosis of two different osteoporotic syndromes: Cushing's syndrome and postmenopausal osteoporosis,” Journal of Endocrinological Investigation, vol. 27, no. 6, pp. 510–515, 2004.
- H. Xu, D. H. Xiong, F. H. Xu, Y. Y. Zhang, S. F. Lei, and H. W. Deng, “Association between VDR ApaI polymorphism and hip bone mineral density can be modified by body mass index: a study on postmenopausal Chinese women,” Acta Biochimica et Biophysica Sinica, vol. 37, no. 1, pp. 61–67, 2005.
- G. T. Lin, H. F. Tseng, C. K. Chang et al., “SNP combinations in chromosome-wide genes are associated with bone mineral density in Taiwanese women,” Chinese Journal of Physiology, vol. 51, no. 1, pp. 32–41, 2008.
- B. L. M. Hogan, “Bone morphogenetic proteins: multifunctional regulators of vertebrate development,” Genes and Development, vol. 10, no. 13, pp. 1580–1594, 1996.
- D. E. Hughes, A. Dai, J. C. Tiffee, H. H. Li, G. R. Munoy, and B. F. Boyce, “Estrogen promotes apoptosis of murine osteoclasts mediated by TGF-β,” Nature Medicine, vol. 2, no. 10, pp. 1132–1135, 1996.
- E. Phelps, O. Bezouglaia, S. Tetradis, and J. M. Nervina, “Parathyroid hormone induces receptor activity modifying protein-3 (RAMP3) expression primarily via -cyclic adenosine monophosphate signaling in osteoblasts,” Calcified Tissue International, vol. 77, no. 2, pp. 96–103, 2005.
- X. Feng, S. Bonni, and K. Riabowol, “HSP70 induction by ING proteins sensitizes cells to tumor necrosis factor alpha receptor-mediated apoptosis,” Molecular and Cellular Biology, vol. 26, no. 24, pp. 9244–9255, 2006.
- Y. Ishida, T. Kondo, A. Kimura, K. Matsushima, and N. Mukaida, “Absence of IL-1 receptor antagonist impaired wound healing along with aberrant NF-κB activation and a reciprocal suppression of TGF-β signal pathway,” Journal of Immunology, vol. 176, no. 9, pp. 5598–5606, 2006.
- S. Hong, S. Lim, A. G. Li et al., “Smad7 binds to the adaptors TAB2 and TAB3 to block recruitment of the kinase TAK1 to the adaptor TRAF2,” Nature Immunology, vol. 8, no. 5, pp. 504–513, 2007.
- T. Mukai, F. Otsuka, H. Otani et al., “TNFα inhibits BMP-induced osteoblast differentiation through activating SAPK/JNK signaling,” Biochemical and Biophysical Research Communications, vol. 356, no. 4, pp. 1004–1010, 2007.
- W. F. Boron and E. L. Boulpaep, Medical Physiology: A Cellular and Molecular Approach, Elsevier Saunders, New York, NY, USA, 2004.
- N. K. Lee, H. Sowa, E. Hinoi et al., “Endocrine regulation of energy metabolism by the skeleton,” Cell, vol. 130, no. 3, pp. 456–469, 2007.
- H. W. Chang, L. Y. Chuang, C. H. Ho, P. L. Chang, and C. H. Yang, “Odds ratio-based genetic algorithms for generating SNP barcodes of genotypes to predict disease susceptibility,” OMICS: A Journal of Integrative Biology, vol. 12, no. 1, pp. 71–81, 2008.
- K. E. Lee, N. Sha, E. R. Dougherty, M. Vannucci, and B. K. Mallick, “Gene selection: a Bayesian variable selection approach,” Bioinformatics, vol. 19, no. 1, pp. 90–97, 2003.
- L. C. Huang, S. Y. Hsu, and E. Lin, “A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data,” Journal of Translational Medicine, vol. 7, p. 81, 2009.
- E. Lin, Y. Hwang, K. H. Liang, and E. Y. Chen, “Pattern-recognition techniques with haplotype analysis in pharmacogenomics,” Pharmacogenomics, vol. 8, no. 1, pp. 75–83, 2007.
- R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997.
- J. T. Tsai, J. H. Chou, and T. K. Liu, “Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm,” IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 69–80, 2006.
- W. H. Ho and C. S. Chang, “Genetic-algorithm-based artificial neural network modeling for platelet transfusion requirements on acute myeloblastic leukemia patients,” Expert Systems with Applications, vol. 38, no. 5, pp. 6319–6323, 2011.
- W. H. Ho, J. X. Chen, I. N. Lee, and H. C. Su, “An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm,” Expert Systems with Applications, vol. 38, no. 10, pp. 13050–13056, 2011.
- Z. Zhang, Y. Ge, D. Zhang, and X. Zhou, “High-content analysis in monastrol suppressor screens: a neural network-based classification approach,” Methods of Information in Medicine, vol. 50, no. 3, pp. 265–272, 2011.
- W. H. Ho, K. T. Lee, H. Y. Chen, T. W. Ho, and H. C. Chiu, “Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network,” PLoS ONE, vol. 7, no. 1, Article ID e29179, 2012.
- E. Lin and L. C. Huang, “Identification of significant genes in genomics using Bayesian variable selection methods,” Advances and Applications in Bioinformatics and Chemistry, vol. 1, pp. 13–18, 2008.
- W. S. Ke, Y. Hwang, and E. Lin, “Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms,” Advances and Applications in Bioinformatics and Chemistry, vol. 3, pp. 39–44, 2010.
- J. T. Tsai, T. K. Liu, and J. H. Chou, “Hybrid Taguchi-genetic algorithm for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 365–377, 2004.
- W. H. Ho, J. H. Chou, and C. Y. Guo, “Parameter identification of chaotic systems using improved differential evolution algorithm,” Nonlinear Dynamics, vol. 61, no. 1-2, pp. 29–41, 2010.
- C. J. Robinson, S. Swift, D. D. Johnson, and J. S. Almeida, “Prediction of pelvic organ prolapse using an artificial neural network,” American Journal of Obstetrics and Gynecology, vol. 199, no. 2, pp. 193–e1, 2008.
- P. Domingos and M. Pazzani, “On the optimality of the simple Bayesian classifier under zero-one loss,” Machine Learning, vol. 29, no. 2-3, pp. 103–130, 1997.
- Q. Wang, G. M. Garrity, J. M. Tiedje, and J. R. Cole, “Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy,” Applied and Environmental Microbiology, vol. 73, no. 16, pp. 5261–5267, 2007.
- M. Bhandari and A. Joensson, Clinical Research for Surgeons, Thieme, New York, NY, USA, 2009.
- I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2005.
- J. M. Koh, Y. H. Khang, C. H. Jung et al., “Higher circulating hsCRP levels are associated with lower bone mineral density in healthy pre- and postmenopausal women: evidence for a link between systemic inflammation and osteoporosis,” Osteoporosis International, vol. 16, no. 10, pp. 1263–1271, 2005.
- J. Y. Y. Leung, A. Y. Y. Ho, T. P. Ip, G. Lee, and A. W. C. Kung, “The efficacy and tolerability of risedronate on bone mineral density and bone turnover markers in osteoporotic Chinese women: a randomized placebo-controlled study,” Bone, vol. 36, no. 2, pp. 358–364, 2005.
- K. M. Summers and T. P. Brock, “Impact of pharmacist-led community bone mineral density screenings,” Annals of Pharmacotherapy, vol. 39, no. 2, pp. 243–248, 2005.
- A. Cucchetti, F. Piscaglia, A. D. Grigioni et al., “Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study,” Journal of Hepatology, vol. 52, no. 6, pp. 880–888, 2010.
- T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.
- R. Hewett and P. Kijsanayothin, “Tumor classification ranking from microarray data,” BMC Genomics, vol. 9, no. 2, article S21, 2008.
- C. F. Aliferis, A. Statnikov, I. Tsamardinos, J. S. Schildcrout, B. E. Shepherd, and F. E. Harrell Jr., “Factors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray data,” PLoS ONE, vol. 4, no. 3, Article ID e4922, 2009.
- Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007.