Artificial neural network predictive model for allergic disease using single nucleotide polymorphisms data

Shuta Tomida, Taizo Hanai, Naoki Koma, Youichi Suzuki, Takeshi Kobayashi, Hiroyuki Honda

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


The purpose of this study was to develop a novel diagnostic prediction method for allergic diseases from the data of single nucleotide polymorphisms (SNPs) using an artificial neural network (ANN). We applied the prediction method to four allergic diseases, such as atopic dermatitis (AD), allergic conjunctivitis (AC), allergic rhinitis (AR) and bronchial asthma (BA), and verified its predictive ability. Almost all the learning data were precisely predicted. Regarding the evaluation data, the learned ANN model could correctly predict a diagnosis with more than 78% accuracy. We also analyzed the SNP data using multiple regression analysis (MRA). Using the MRA model, less than 10% of patients with the above allergic diseases were correctly diagnosed, while this figure was more than 75% for persons without allergic diseases. From these results, it was shown that the ANN model was superior to the MRA model with respect to predictive ability of allergic diseases. Moreover, we used two different methods to convert the genetic polymorphism data into numerical data. Using both methods, diagnostic predictions were quite precise and almost the same predictive abilities were observed. This is the first study showing the application and usefulness of an ANN for the prediction of allergic diseases based on SNP data.

Original languageEnglish
Pages (from-to)470-478
Number of pages9
JournalJournal of Bioscience and Bioengineering
Issue number5
Publication statusPublished - 2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology


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