Detecting and discovering new gene interactions based on known gene expressions and gene interaction data presents a significant challenge. Various statistical and deep learning methods have attempted to tackle this challenge by leveraging the topological structure of gene interactions and gene expression patterns to predict novel gene interactions. In contrast, some approaches have focused exclusively on utilizing gene expression profiles. In this context, we introduce GENER, a parallel-layer deep learning network designed exclusively for the identification of gene-gene relationships using gene expression data. We conducted two training experiments and compared the performance of our network with that of existing statistical and deep learning approaches. Notably, our model achieved an average AUROC score of 0.834 on the combined BioGRID&DREAM5 dataset, outperforming competing methods in predicting gene-gene interactions.

翻译：从已知基因表达和基因相互作用数据中检测并发现新的基因相互作用是一项重大挑战。多种统计学和深度学习方法尝试利用基因相互作用的拓扑结构和基因表达模式来预测新型基因相互作用，从而应对这一挑战。相比之下，部分方法仅专注于使用基因表达谱。在此背景下，我们提出了GENER——一种专为利用基因表达数据识别基因-基因关系而设计的并行层深度学习网络。我们进行了两次训练实验，并将网络的性能与现有统计学和深度学习方法进行了比较。值得注意的是，在合并的BioGRID&DREAM5数据集上，我们的模型平均AUROC得分为0.834，在预测基因-基因相互作用方面优于其他竞争方法。