Abstract:
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We consider an application of Graph Convolutional Neural Networks (GCNNs) for phenotype prediction with genomic data. In a standard GCNN, there is only one graph or network to describe the relationships among the predictors. However, for genomic applications, due to condition- or tissue-specific gene function and genetic regulation, we may have multiple gene networks. We develop a new method to incorporate multiple gene networks into a GCNN for genomic prediction.
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