GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation

TL;DR

GeSubNet is a novel framework that integrates gene interaction knowledge and disease subtype information to generate accurate, subtype-specific gene networks, improving upon existing methods in cancer datasets.

Contribution

We introduce GeSubNet, a multi-step learning framework that combines patient gene expression profiles and prior gene networks to produce subtype-specific gene interaction networks.

Findings

Outperforms traditional methods with over 20-50% improvement in graph evaluation metrics.

Successfully identifies subtype-specific genes with 83% likelihood of influencing patient distribution.

Demonstrates effectiveness across four cancer datasets.

Abstract

Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unified representation capable of predicting gene interactions while distinguishing between different disease subtypes. Graphs generated by such representations can be considered subtype-specific networks. GeSubNet is a multi-step representation learning framework with three modules: First, a deep generative model learns distinct disease subtypes from patient gene expression profiles. Second, a graph neural network captures representations of prior gene networks from knowledge databases, ensuring accurate physical gene interactions. Finally, we integrate these two representations using an inference loss that leverages graph generation capabilities, conditioned on the patient separation loss, to refine subtype-specific information in the learned representation. GeSubNet consistently outperforms traditional methods, with average improvements of 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics, averaged over four cancer datasets. Particularly, we conduct a biological simulation experiment to assess how the behavior of selected genes from over 11,000 candidates affects subtypes or patient distributions. The results show that the generated network has the potential to identify subtype-specific genes with an 83% likelihood of impacting patient distribution shifts.