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Linked References

1. 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. View at Scopus
2. 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. View at Publisher · View at Google Scholar · View at Scopus
3. 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. View at Scopus
4. 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. View at Scopus
5. 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. View at Scopus
6. 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. View at Scopus
7. 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. View at Scopus
8. 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. View at Scopus
9. 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. View at Scopus
10. B. L. M. Hogan, “Bone morphogenetic proteins: multifunctional regulators of vertebrate development,” Genes and Development, vol. 10, no. 13, pp. 1580–1594, 1996. View at Scopus
11. 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. View at Publisher · View at Google Scholar · View at Scopus
12. E. Phelps, O. Bezouglaia, S. Tetradis, and J. M. Nervina, “Parathyroid hormone induces receptor activity modifying protein-3 (RAMP3) expression primarily via $3^{\prime },5^{\prime }$-cyclic adenosine monophosphate signaling in osteoblasts,” Calcified Tissue International, vol. 77, no. 2, pp. 96–103, 2005. View at Publisher · View at Google Scholar · View at Scopus
13. 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. View at Publisher · View at Google Scholar · View at Scopus
14. 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. View at Scopus
15. 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. View at Publisher · View at Google Scholar · View at Scopus
16. 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. View at Publisher · View at Google Scholar · View at Scopus
17. W. F. Boron and E. L. Boulpaep, Medical Physiology: A Cellular and Molecular Approach, Elsevier Saunders, New York, NY, USA, 2004.
18. 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. View at Publisher · View at Google Scholar · View at Scopus
19. 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. View at Publisher · View at Google Scholar · View at Scopus
20. 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. View at Publisher · View at Google Scholar · View at Scopus
21. 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. View at Scopus
22. 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. View at Publisher · View at Google Scholar · View at Scopus
23. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997. View at Scopus
24. 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. View at Publisher · View at Google Scholar · View at Scopus
25. 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. View at Publisher · View at Google Scholar · View at Scopus
26. 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. View at Publisher · View at Google Scholar · View at Scopus
27. 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. View at Publisher · View at Google Scholar
28. 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. View at Publisher · View at Google Scholar
29. 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.
30. 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.
31. 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. View at Publisher · View at Google Scholar · View at Scopus
32. 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. View at Publisher · View at Google Scholar · View at Scopus
33. 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. View at Publisher · View at Google Scholar · View at Scopus
34. 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. View at Scopus
35. 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. View at Publisher · View at Google Scholar · View at Scopus
36. M. Bhandari and A. Joensson, Clinical Research for Surgeons, Thieme, New York, NY, USA, 2009.
37. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 2005.
38. 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. View at Publisher · View at Google Scholar · View at Scopus
39. 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. View at Publisher · View at Google Scholar · View at Scopus
40. 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. View at Publisher · View at Google Scholar · View at Scopus
41. 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. View at Publisher · View at Google Scholar · View at Scopus
42. T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006. View at Publisher · View at Google Scholar · View at Scopus
43. R. Hewett and P. Kijsanayothin, “Tumor classification ranking from microarray data,” BMC Genomics, vol. 9, no. 2, article S21, 2008. View at Publisher · View at Google Scholar · View at Scopus
44. 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. View at Publisher · View at Google Scholar · View at Scopus
45. Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007. View at Publisher · View at Google Scholar · View at Scopus