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Detecting and discovering new gene interactions based on known geneexpressions and gene interaction data presents a significant challenge. Variousstatistical and deep learning methods have attempted to tackle this challengeby leveraging the topological structure of gene interactions and geneexpression patterns to predict novel gene interactions. In contrast, someapproaches have focused exclusively on utilizing gene expression profiles. Inthis context, we introduce GENER, a parallel-layer deep learning networkdesigned exclusively for the identification of gene-gene relationships usinggene expression data. We conducted two training experiments and compared theperformance of our network with that of existing statistical and deep learningapproaches. Notably, our model achieved an average AUROC score of 0.834 on thecombined BioGRID&DREAM5 dataset, outperforming competing methods in predictinggene-gene interactions.

GENER: A Parallel Layer Deep Learning Network To Detect Gene-Gene Interactions From Gene Expression Data