Skip to main content

Advertisement

Springer Nature Link
Log in

The Association Forecasting of 13 Variants Within Seven Asthma Susceptibility Genes on 3 Serum IgE Groups in Taiwanese Population by Integrating of Adaptive Neuro-fuzzy Inference System (ANFIS) and Classification Analysis Methods

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Asthma is one of the most common chronic diseases in children. It is caused by complicated coactions between various genetic factors and environmental allergens. The study aims to integrate the concept of implementing adaptive neuro-fuzzy inference system (ANFIS) and classification analysis methods for forecasting the association of asthma susceptibility genes on 3 serum IgE groups. The ANFIS model was trained and tested with data sets obtained from 425 asthmatic subjects and 483 non-asthma subjects from the Taiwanese population. We assessed 13 single-nucleotide polymorphisms (SNPs) in seven well-known asthma susceptibility genes; firstly, the proposed ANFIS model learned to reduce input features from the 13 SNPs. And secondly, the classification will be used to classify the serum IgE groups from the simulated SNPs results. The performance of the ANFIS model, classification accuracies and the results confirmed that the integration of ANFIS and classified analysis has potential in association discovery.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Mobley, B. A., Schechter, E., Moore, W. E., McKee, P. A., and Eichner, J. E., Predictions of coronary artery stenosis by artificial neural network. Artif. Intell. Med. 18:187–203, 2000.

    Article  Google Scholar 

  2. Miller, A. S., Blott, B. H., and Hames, T. K., Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30:449–464, 1992.

    Article  Google Scholar 

  3. Übeyli, E. D., Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals. Comput. Biol. Med. 38:5563–5573, 2008.

    Google Scholar 

  4. Übeyli, E. D., Comparison of different classification algorithms in clinical decision-making. Expert Syst. 24:117–131, 2007.

    Google Scholar 

  5. Übeyli, E. D., Detection of electrocardiogram beats using a fuzzy similarity index. Expert Syst. 24:287–296, 2007.

    Google Scholar 

  6. Übeyli, E. D., Combining neural network models for automated diagnostic systems. J. Med. Syst. 30:6483–6488, 2006.

    Google Scholar 

  7. Übeyli, E. D., Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents. Digit. Signal Process. 18:4646–4656, 2008.

    Google Scholar 

  8. Baxt, W. G., Use of an artificial neural network for data analysis in clinical decision making: The diagnosis of acute coronary occlusion. Neural Comput. 2:480–489, 1990.

    Article  Google Scholar 

  9. Ergün, U., Serhatlioglu, S., Hardalac, F., and Guler, I., Classification of carotid artery stenosis of the patients with diabetes by neural network and logistic regression. Comput. Biol. Med. 34:389–405, 2004.

    Article  Google Scholar 

  10. Güler, I., and Übeyli, E. D., Detection of ophthalmic artery stenosis by least-mean squares back propagation neural network. Comput. Biol. Med. 33:333–343, 2003.

    Article  Google Scholar 

  11. Dubois, D., and Prade, H., An introduction to fuzzy systems. Clin. Chim. Acta 270:3–29, 1998.

    Article  Google Scholar 

  12. Kuncheva, L. I., and Steimann, F., Fuzzy diagnosis. Art. Intell. Med. 16:121–128, 1999.

    Article  Google Scholar 

  13. Nauck, D., and Kruse, R., Obtaining interpretable fuzzy classification rules from medical data. Art. Intell. Med. 16:149–169, 1999.

    Article  Google Scholar 

  14. Güler, I., Hardalac, F., Ergün, U., and Baris-C, N., Application of FFT analyzed cardiac Doppler signals to fuzzy algorithm. Comput. Biol. Med. 32:435–444, 2002.

    Article  Google Scholar 

  15. Güler, I., Hardalac, F., Ergün, U., and Baris-C, N., Classification of aorta Doppler signals using variable coded hierarchical genetic fuzzy system. Expert Syst. Appl. 26:321–333, 2004.

    Article  Google Scholar 

  16. Andrews, R., Diederich, J., and Tickle, A., A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl. Base Syst. 8:373–389, 1995.

    Article  Google Scholar 

  17. Mcgarry, K., Wermter, S., and Macintyre, J., The extraction and comparison of knowledge from local function networks. Int. J. Comput. Intell. Appl. 1:369–382, 2001.

    Article  Google Scholar 

  18. Jang, J. S. R., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23:3665–3685, 1993.

    Article  Google Scholar 

  19. Usher, J., Campbell, D., Vohra, J., and Cameron, J., A fuzzy logic controlled classifier for use in implantable cardioverter defibrillators. Pacing Clin. Electrophysiol. 22:183–186, 1999.

    Article  Google Scholar 

  20. Belal, S. Y., Taktak, A. F. G., Nevill, A. J., Spencer, S. A., Roden, D., and Bevan, S., Automatic detection of distorted plethysmogram pulses in neonates and pediatric patients using an adaptive network-based fuzzy inference system. Artif. Intell. Med. 24:149–165, 2002.

    Article  Google Scholar 

  21. Virant-Klun, I., and Virant, J., Fuzzy logic alternative for analysis in the biomedical sciences. Comput. Biomed. Res. 32:305–321, 1999.

    Article  Google Scholar 

  22. Masoli, M., Fabian, D., Holt, S., and Beasley, R., The global burden of asthma: Executive summary of GINA Dissemination Committee Report. Allergy 59:469–478, 2004.

    Article  Google Scholar 

  23. Beasley, R., The burden of asthma with specific reference to the United States. J. Allergy Clin. Immunol. 109:S482–S489, 2002.

    Article  Google Scholar 

  24. Bach, J. F., The effect of infections on susceptibility to autoimmune and allergic diseases. N. Engl. J. Med. 347:911–920, 2002.

    Article  Google Scholar 

  25. Skadhauge, L. R., Christensen, K., Kyvik, K. O., and Sigsgaard, T., Genetic and environmental influence on asthma: A population-based study of 11688 Danish twin pairs. Eur. Respir. J. 13:8–14, 1999.

    Article  Google Scholar 

  26. Ober, C., and Hoffjan, S., Asthma genetics 2006: The long and winding road to gene discovery. Genes Immun. 7:95–100, 2006.

    Article  Google Scholar 

  27. Anderson, G. G., and Cookson, W. O., Recent advanced in the genetics of allergy and asthma. Mol. Med. Today 5:264–273, 1999.

    Article  Google Scholar 

  28. Blumenthal, M. M., What we know about the genetics of asthma at the beginning of the 21st century. Clin. Rev. Allergy Immunol. 22:11–31, 2002.

    Article  Google Scholar 

  29. Holloway, J. W., Beghe, B., and Holgate, S. T., The genetic basis of atopic asthma. Clin. Exp. Allergy 29:1023–1032, 1999.

    Article  Google Scholar 

  30. Huang, J. L., Asthma severity and genetics in Taiwan. J. Microbiol. Immunol. Infect. 38:158–163, 2005.

    Google Scholar 

  31. Burney, P. G., Luczynska, C., Chinn, S., and Jarvis, D., The European Community Respiratory Health Survey. Eur. Respir. J. 5:954–960, 1994.

    Article  Google Scholar 

  32. Pearce, N., Sunyer, J., Cheng, S., Chinn, S., Bjorksten, B., Burr, M., Keil, U., Anderson, H. R., and Burney, P., Comparison of asthma prevalence in the ISAAC and the ECRHS. ISAAC Steering Committee and the European Community Respiratory Health Survey. International Study of Asthma and Allergies in Childhood. Eur. Respir. J. 16:420–426, 2000.

    Article  Google Scholar 

  33. Beydon, N., Davis, S. D., Lombardi, E., Allen, J. L., Arets, H. G. M., Aurora, P., Bisgaard, H., Davis, G. M., Ducharme, F. M., Eigen, H., Gappa, M., Gaultier, C., Gustafsson, P. M., Hall, G. L., Hantos, Z., Healy, M. J. R., Jones, M. H., Klug, B., Carlsen, K. C. L., McKenzie, S. A., Marchal, F., Mayer, O. H., Merkus, P. J. F. M., Morris, M. G., Oostveen, E., Pillow, J. J., Seddon, P. C., Silverman, M., Sly, P. D., Stocks, J., Tepper, R. S., Vilozni, D., and Wilson, N. H., Behalf of the American Thoracic Society/European Respiratory Society Working Group on Infant and Young Children Pulmonary Function Testing. An official American Thoracic Society/European Respiratory Society Statement: Pulmonary function testing in preschool children. Am. J. Respir. Crit. Care Med. 175:1304–1345, 2007.

    Article  Google Scholar 

  34. Terano, T., Asai, K., and Sugeno, M., Fuzzy systems theory and its applications. Academic, San Diego, 1992.

    MATH  Google Scholar 

  35. Mamdani, E. H., and Assilian, S., An experiment in linquistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7:1–8, 1975.

    Article  MATH  Google Scholar 

  36. Sugeno, M., and Kang, G. T., Structure identification of fuzzy model. Fuzzy Sets Syst. 28:15–33, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  37. Leung, T. F., Tang, N. L. S., Chan, H. I. S., Li, A. M., Ha, G., Lam, C. W. K., and Fok, T. F., Distribution in allele frequencies of predisposition-to atopy genotypes in Chinese children. Pediatr. Pulmonol. 34:419–424, 2002.

    Article  Google Scholar 

  38. Unoki, M., Furuta, S., Onouchi, Y., Watanabe, O., Doi, S., Fujiwara, H., Miyatake, A., Fujita, K., Tamari, M., and Nakamura, Y., Association studies of 33 single nucleotide polymorphisms (SNPs) in 29 candidate genes for bronchial asthma: Positive association a T294C polymorphism in the thromboxane A2 receptor gene. Hum. Genet. 106:440–446, 2000.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the editors and anonymous referees for their helpful comments on this paper, and also thankful to Miss Rebecca Cheng for clerical assistance.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan

    Cheng-Hang Wang & Baw-Jhiune Liu

  2. Institute of Medical Sciences, Tzu Chi University, No.701, Zhongyang Rd., Sec 3, Hualien, 97004, Taiwan

    Lawrence Shih-Hsin Wu

Authors
  1. Cheng-Hang Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Baw-Jhiune Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Lawrence Shih-Hsin Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Lawrence Shih-Hsin Wu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, CH., Liu, BJ. & Wu, L.SH. The Association Forecasting of 13 Variants Within Seven Asthma Susceptibility Genes on 3 Serum IgE Groups in Taiwanese Population by Integrating of Adaptive Neuro-fuzzy Inference System (ANFIS) and Classification Analysis Methods. J Med Syst 36, 175–185 (2012). https://doi.org/10.1007/s10916-010-9457-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-010-9457-4

Keywords