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

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

doi:10.1016/s1389-1723(02)80094-9

Cited by 24 publications

(11 citation statements)

References 27 publications

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“…[24][25][26][27] It was able to process data containing complex (nonlinear) relationships and interactions that were often too difficult or complex to interpret by conventional linear methods. 28 Meanwhile, BPANN did not require variables to satisfy normality and independence conditions and could process collinearity questions between variables.…”

Section: Discussionmentioning

confidence: 99%

Application of Back Propagation Artificial Neural Network on Genetic Variants in Adiponectin ADIPOQ, Peroxisome Proliferator-Activated Receptor-γ, and Retinoid X Receptor-α Genes and Type 2 Diabetes Risk in a Chinese Han Population

Shi

et al. 2012

Diabetes Technology & Therapeutics

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Section: Discussionmentioning

confidence: 99%

Application of Back Propagation Artificial Neural Network on Genetic Variants in Adiponectin ADIPOQ, Peroxisome Proliferator-Activated Receptor-γ, and Retinoid X Receptor-α Genes and Type 2 Diabetes Risk in a Chinese Han Population

Shi

et al. 2012

Diabetes Technology & Therapeutics

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“…This type of information can assign predetermined weights and adjust the statistical significance necessary to conclude that the gene is indeed an important disease modulator. Other suggested approaches involve clustering genes based on degrees of association [89] or using neural networks [90], which have a unique ability to handle multiple interaction terms.…”

Section: Future Of Genetic Analysismentioning

confidence: 99%

Genetics of Arrhythmia: Disease Pathways Beyond Ion Channels

Pérez

Wheeler

et al. 2008

J. of Cardiovasc. Trans. Res.

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Diseases of the electrical conduction system that lead to irregularities in cardiac rhythm can have morbid and often lethal clinical outcomes. Linkage analysis has been the principal tool used to discover the genetic mutations responsible for Mendelian arrhythmic disease. Although the majority of arrhythmias can be accounted for by mutations in genes encoding ion channels, linkage analysis has also uncovered the role of other gene families such as those encoding members of the desmosome. With a list of candidates in mind, mutational analysis has helped confirm the suspicion that proteins found in caveolae or gap junctions also play a role in arrhythmogenesis. Atrial fibrillation and sudden cardiac death are relatively common arrhythmias that may be caused by multiple factors including common genetic variants. Genome-wide association studies are already revealing the important and poorly understood role of intergenic regions in atrial fibrillation. Despite the great advancements that have been made in our understanding of the genetics of these diseases, we are still far from able to routinely use genomic data to make clinical management decisions. There remain several hurdles in the study of genetics of arrhythmia, including the costs of genotyping, the need to find large affected families for linkage analysis, or to recruit large numbers of patients for genome-wide studies. Novel techniques that incorporate epigenetic information, such as known gene-gene interactions, biologic pathways, and experimental gene expression, will need to be developed to better interpret the large amount of genetic data that can now be generated. The study of arrhythmia genetics will continue to elucidate the pathophysiology of disease, help identify novel therapies, and ultimately allow us to deliver the individualized medical therapy that has long been anticipated.

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“…Xie presented an adaptation of the decision forest pattern recognition algorithm for esophageal cancer association studies [13]. Yasuyuki et al and Shuta et al proposed selection of susceptible single nucleotide polymorphisms and construction of allergic asthma prediction model using artificial neural network [14,15].…”

Section: Introductionmentioning

confidence: 99%

USVM: Selection of SNPs in Diseases Association Study Using UMDA and SVM

Wei

Peng

et al. 2010

2010 4th International Conference on Bioinformatics and Biomedical Engineering

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With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. One main challenge for this study is to find the optimal subsets of Single Nucleotide Polymorphisms (SNPs) which are most tightly associated with diseases. Feature selection has become a necessity in many bioinformatics applications. In this paper, we propose a wrapper algorithm named USVM which combines Univariate Marginal Distribution Algorithm (UMDA) and Support Vector Machine (SVM) for disease association study. USVM not only eliminates the redundancy of feature, but also solves the problem of SVM's parameters selection. We use USVM to analyze the Crohn's disease (CD) dataset including 387 samples and each one has 103 SNPs. The experimental results show that our algorithm outperforms the current algorithms including DNF, CSP, ORF and so on.

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

Cited by 24 publications

References 27 publications

Application of Back Propagation Artificial Neural Network on Genetic Variants in Adiponectin ADIPOQ, Peroxisome Proliferator-Activated Receptor-γ, and Retinoid X Receptor-α Genes and Type 2 Diabetes Risk in a Chinese Han Population

Application of Back Propagation Artificial Neural Network on Genetic Variants in Adiponectin ADIPOQ, Peroxisome Proliferator-Activated Receptor-γ, and Retinoid X Receptor-α Genes and Type 2 Diabetes Risk in a Chinese Han Population

Genetics of Arrhythmia: Disease Pathways Beyond Ion Channels

USVM: Selection of SNPs in Diseases Association Study Using UMDA and SVM

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