Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma.
Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma.
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DOI：
10.1186/1471-2105-5-120
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发表时间：
2004-09-01
影响因子：
3
通讯作者：
Honda H
中科院分区：
文献类型：
--
作者：
Tomita Y;Tomida S;Hasegawa Y;Suzuki Y;Shirakawa T;Kobayashi T;Honda H
筛选各种基因标记，如单核苷酸多态性（SNP）以及这些标记与多因子疾病发展之间的相关性，此前已经进行了研究。在此，我们提出了一种易感标记选择人工神经网络（ANN）来预测过敏性疾病的发展。为了预测儿童过敏性哮喘（CAA）的发展并筛选易感snp，我们采用带有参数递减法（PDM）的人工神经网络对344名日本人的17个基因的25个snp进行了分析，筛选出10个CAA易感snp。10个snp的ANN模型对学习数据的准确率为97.7%，对评价数据的准确率为74.4%。本文定义了有效组合值（ECV）来确定重要组合。有效的2-SNP或3-SNP组合集中在10个选定的snp中。人工神经网络可以可靠地选择与CAA相关的SNP组合。因此，人工神经网络可以用来表征由多种因素引起的复杂疾病的发展。这是首次从300多例患者的SNP数据中自动选择与多因子疾病发展相关的SNP。
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. 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. 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.