GENER: A Parallel Layer Deep Learning Network To Detect Gene-Gene Interactions From Gene Expression Data
This work addresses the challenge of identifying gene interactions for biomedical research, but it appears incremental as it builds on existing deep learning approaches with a focus on gene expression data.
The paper tackled the problem of detecting gene-gene interactions from gene expression data by introducing GENER, a parallel-layer deep learning network, which achieved an average AUROC score of 0.834 on the BioGRID&DREAM5 dataset, outperforming existing methods.
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.