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

TL;DR

GENER is a parallel-layer deep learning network that effectively detects gene-gene interactions from gene expression data, outperforming existing methods with an AUROC of 0.834 on benchmark datasets.

Contribution

The paper introduces GENER, a novel parallel-layer deep learning model specifically designed for gene-gene interaction prediction from expression data, demonstrating superior performance.

Findings

Achieved an average AUROC of 0.834 on BioGRID&DREAM5 datasets.

Outperformed existing statistical and deep learning methods.

Validated effectiveness through two training experiments.

Abstract

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.