Skip to main content
Top
Published in: Journal of Medical Systems 1/2012

01-02-2012 | Original Paper

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

Authors: Cheng-Hang Wang, Baw-Jhiune Liu, Lawrence Shih-Hsin Wu

Published in: Journal of Medical Systems | Issue 1/2012

Login to get access

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.
Literature
1.
go back to reference 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.CrossRef 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.CrossRef
2.
go back to reference 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.CrossRef 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.CrossRef
3.
go back to reference Übeyli, E. D., Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals. Comput. Biol. Med. 38:5563–5573, 2008. Übeyli, E. D., Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals. Comput. Biol. Med. 38:5563–5573, 2008.
4.
go back to reference Übeyli, E. D., Comparison of different classification algorithms in clinical decision-making. Expert Syst. 24:117–131, 2007. Übeyli, E. D., Comparison of different classification algorithms in clinical decision-making. Expert Syst. 24:117–131, 2007.
5.
go back to reference Übeyli, E. D., Detection of electrocardiogram beats using a fuzzy similarity index. Expert Syst. 24:287–296, 2007. Übeyli, E. D., Detection of electrocardiogram beats using a fuzzy similarity index. Expert Syst. 24:287–296, 2007.
6.
go back to reference Übeyli, E. D., Combining neural network models for automated diagnostic systems. J. Med. Syst. 30:6483–6488, 2006. Übeyli, E. D., Combining neural network models for automated diagnostic systems. J. Med. Syst. 30:6483–6488, 2006.
7.
go back to reference Übeyli, E. D., Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents. Digit. Signal Process. 18:4646–4656, 2008. Übeyli, E. D., Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents. Digit. Signal Process. 18:4646–4656, 2008.
8.
go back to reference 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.CrossRef 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.CrossRef
9.
go back to reference 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.CrossRef 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.CrossRef
10.
go back to reference 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.CrossRef 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.CrossRef
11.
go back to reference Dubois, D., and Prade, H., An introduction to fuzzy systems. Clin. Chim. Acta 270:3–29, 1998.CrossRef Dubois, D., and Prade, H., An introduction to fuzzy systems. Clin. Chim. Acta 270:3–29, 1998.CrossRef
12.
go back to reference Kuncheva, L. I., and Steimann, F., Fuzzy diagnosis. Art. Intell. Med. 16:121–128, 1999.CrossRef Kuncheva, L. I., and Steimann, F., Fuzzy diagnosis. Art. Intell. Med. 16:121–128, 1999.CrossRef
13.
go back to reference Nauck, D., and Kruse, R., Obtaining interpretable fuzzy classification rules from medical data. Art. Intell. Med. 16:149–169, 1999.CrossRef Nauck, D., and Kruse, R., Obtaining interpretable fuzzy classification rules from medical data. Art. Intell. Med. 16:149–169, 1999.CrossRef
14.
go back to reference 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.CrossRef 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.CrossRef
15.
go back to reference 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.CrossRef 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.CrossRef
16.
go back to reference 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.CrossRef 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.CrossRef
17.
go back to reference 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.CrossRef 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.CrossRef
18.
go back to reference Jang, J. S. R., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23:3665–3685, 1993.CrossRef Jang, J. S. R., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23:3665–3685, 1993.CrossRef
19.
go back to reference 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.CrossRef 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.CrossRef
20.
go back to reference 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.CrossRef 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.CrossRef
21.
go back to reference Virant-Klun, I., and Virant, J., Fuzzy logic alternative for analysis in the biomedical sciences. Comput. Biomed. Res. 32:305–321, 1999.CrossRef Virant-Klun, I., and Virant, J., Fuzzy logic alternative for analysis in the biomedical sciences. Comput. Biomed. Res. 32:305–321, 1999.CrossRef
22.
go back to reference 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.CrossRef 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.CrossRef
23.
go back to reference Beasley, R., The burden of asthma with specific reference to the United States. J. Allergy Clin. Immunol. 109:S482–S489, 2002.CrossRef Beasley, R., The burden of asthma with specific reference to the United States. J. Allergy Clin. Immunol. 109:S482–S489, 2002.CrossRef
24.
go back to reference Bach, J. F., The effect of infections on susceptibility to autoimmune and allergic diseases. N. Engl. J. Med. 347:911–920, 2002.CrossRef Bach, J. F., The effect of infections on susceptibility to autoimmune and allergic diseases. N. Engl. J. Med. 347:911–920, 2002.CrossRef
25.
go back to reference 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.CrossRef 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.CrossRef
26.
go back to reference Ober, C., and Hoffjan, S., Asthma genetics 2006: The long and winding road to gene discovery. Genes Immun. 7:95–100, 2006.CrossRef Ober, C., and Hoffjan, S., Asthma genetics 2006: The long and winding road to gene discovery. Genes Immun. 7:95–100, 2006.CrossRef
27.
go back to reference Anderson, G. G., and Cookson, W. O., Recent advanced in the genetics of allergy and asthma. Mol. Med. Today 5:264–273, 1999.CrossRef Anderson, G. G., and Cookson, W. O., Recent advanced in the genetics of allergy and asthma. Mol. Med. Today 5:264–273, 1999.CrossRef
28.
go back to reference 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.CrossRef 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.CrossRef
29.
go back to reference Holloway, J. W., Beghe, B., and Holgate, S. T., The genetic basis of atopic asthma. Clin. Exp. Allergy 29:1023–1032, 1999.CrossRef Holloway, J. W., Beghe, B., and Holgate, S. T., The genetic basis of atopic asthma. Clin. Exp. Allergy 29:1023–1032, 1999.CrossRef
30.
go back to reference Huang, J. L., Asthma severity and genetics in Taiwan. J. Microbiol. Immunol. Infect. 38:158–163, 2005. Huang, J. L., Asthma severity and genetics in Taiwan. J. Microbiol. Immunol. Infect. 38:158–163, 2005.
31.
go back to reference Burney, P. G., Luczynska, C., Chinn, S., and Jarvis, D., The European Community Respiratory Health Survey. Eur. Respir. J. 5:954–960, 1994.CrossRef Burney, P. G., Luczynska, C., Chinn, S., and Jarvis, D., The European Community Respiratory Health Survey. Eur. Respir. J. 5:954–960, 1994.CrossRef
32.
go back to reference 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.CrossRef 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.CrossRef
33.
go back to reference 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.CrossRef 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.CrossRef
34.
go back to reference Terano, T., Asai, K., and Sugeno, M., Fuzzy systems theory and its applications. Academic, San Diego, 1992.MATH Terano, T., Asai, K., and Sugeno, M., Fuzzy systems theory and its applications. Academic, San Diego, 1992.MATH
35.
go back to reference 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.MATHCrossRef 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.MATHCrossRef
37.
go back to reference 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.CrossRef 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.CrossRef
38.
go back to reference 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.CrossRef 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.CrossRef
Metadata
Title
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
Authors
Cheng-Hang Wang
Baw-Jhiune Liu
Lawrence Shih-Hsin Wu
Publication date
01-02-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 1/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-010-9457-4

Other articles of this Issue 1/2012

Journal of Medical Systems 1/2012 Go to the issue