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：基于已知基因表达与基因相互作用数据检测并发现新的基因互作关系是一项重大挑战。各类统计学及深度学习方法尝试通过利用基因相互作用的拓扑结构与基因表达模式来预测新型基因互作关系，而另一些方法则专注于仅使用基因表达谱进行研究。在此背景下，我们提出GENER——一种专为基于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。我们开展了两次训练实验，将本网络与现有统计学及深度学习方法进行性能对比。值得注意的是，在BioGRID与DREAM5的联合数据集上，我们的模型取得了0.834的平均AUROC分数，在预测基因-基因相互作用方面优于其他竞争方法。