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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

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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.

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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.

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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

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  1. Cheng-Hang Wang
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  2. Baw-Jhiune Liu
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Correspondence to Lawrence Shih-Hsin Wu.

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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

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