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No. | Author | Dataset | Description |
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(1) | Ritchie et al. [11] | Epistasis model. | GPNN and BPNN were used to model gene-gene interactions by using simulated data. The simulated data contains functional SNPs and nonfunctional SNPs which model the interaction between genes. |
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(2) | Tomita et al. [12] | Childhood allergic asthma (CAA). | Artificial neural network was utilized with parameter decreasing method in order to analyse susceptible SNPs among the Japanese people. |
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(3) | Keedwell and Narayanan [13] | Artificial data experiments, rat spinal cord and yeast Saccharomyces Cerevisiae cell cycle. | Genetic algorithm which was implemented along with neural networks discovers gene-gene interactions in temporal gene expression dataset by elucidating the information between regulatory connections and interactions between genes, proteins, and other gene products. |
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(4) | Motsinger et al. [14] | Parkinson’s disease. | GPNN had been used to optimize the architecture of neural network. This method can be used to enhance the identification of gene combinations associated with Parkinson’s disease. |
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(5) | Ritchie et al. [15] | Alzheimer’s disease, breast’s disease, colorectal disease, and prostate’s disease. | GPNN had been used to detect gene-gene interactions and gene-environment interaction in studies of human disease to optimize the architecture of Neural Network by using simulated dataset. |
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(6) | Motsinger-Reif et al. [16] | Epitasis model. | GENN was utilized to discover gene-gene interactions that caused are by noise (for instance, genotyping error, missing data, phenocopy, and genetic heterogeneity) in high dimensional genetic epidemiological data. |
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(7) | Günther et al. [17] | Two-locus disease models, multiplicative and epistasis model. | NN had been used in simulation study to model the different kind of two-locus disease model by constructing six neural networks. |
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(8) | Turner et al. [18] | Simulated human. | ATHENA had been used to discover the gene-gene interactions that influence complex human traits by integrating alternative tree-based crossover, back propagation, and domain knowledge in ATHENA. |
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(9) |
Hardison and Motsinger-Reif [4] | Genetic models. | QTGENN had applied GENN methods to quantitative traits in various types of simulated genetic models. This method had been successfully applied in single-locus models and two-locus models. |
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