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Handling these limits, we develop the Heterogeneous built-in Graph for Predicting Disease Genes (HetIG-PreDiG) model which includes gene-gene, gene-disease, and gene-tissue associations. We predict novel disease genes using low-dimensional representation of nodes accounting for network framework, and expanding beyond system construction utilizing the developed Gene-Disease Prioritization Score (GDPS) reflecting the amount of gene-disease relationship via gene co-expression data. For negative education examples, we choose non-associated
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