Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Research article

Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma

Yasuyuki Tomita1, Shuta Tomida1, Yuko Hasegawa1, Yoichi Suzuki2, Taro Shirakawa3, Takeshi Kobayashi1 and Hiroyuki Honda1*

Author Affiliations

1 Department of Biotechnology, School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

2 Department of Medical Genetics, Tohoku University School of Medicine, 1-1 Seiryou-machi, Aoba-ku, Sendai, 980-8575, Japan

3 Department of Health Promotion and Human Behavior, Kyoto University School of Public Health, Yoshida-Konoe cho, Sakyo-ku, Kyoto, 606-8501, Japan

For all author emails, please log on.

BMC Bioinformatics 2004, 5:120  doi:10.1186/1471-2105-5-120

Published: 1 September 2004

Abstract

Background

Screening of various gene markers such as single nucleotide polymorphism (SNP) and correlation between these markers and development of multifactorial disease have previously been studied. Here, we propose a susceptible marker-selectable artificial neural network (ANN) for predicting development of allergic disease.

Results

To predict development of childhood allergic asthma (CAA) and select susceptible SNPs, we used an ANN with a parameter decreasing method (PDM) to analyze 25 SNPs of 17 genes in 344 Japanese people, and select 10 susceptible SNPs of CAA. The accuracy of the ANN model with 10 SNPs was 97.7% for learning data and 74.4% for evaluation data. Important combinations were determined by effective combination value (ECV) defined in the present paper. Effective 2-SNP or 3-SNP combinations were found to be concentrated among the 10 selected SNPs.

Conclusion

ANN can reliably select SNP combinations that are associated with CAA. Thus, the ANN can be used to characterize development of complex diseases caused by multiple factors. This is the first report of automatic selection of SNPs related to development of multifactorial disease from SNP data of more than 300 patients.