---
title: "GDAs-OT: A Prediction Method of Gene-Disease Associations Based on Optimal Transport for Identifying Genes Related to Immune-Related Adverse Events"
authors:
  - "Ruhao Liu"
  - "Suixue Wang"
  - "Hang Yu"
  - "Peng Li"
  - "Qingchen Zhang"
doi: "10.24963/ijcai.2026/623"
url: "https://ijcai.org/proceedings/2026/623"
pdf: "https://ijcai.org/proceedings/2026/0623.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "5594-5602"
track: "Main Track"
keywords:
  - "Multidisciplinary Topics and Applications: Bioinformatics"
  - "Multidisciplinary Topics and Applications: Life sciences"
  - "Multidisciplinary Topics and Applications: Health and medicine"
---

# GDAs-OT: A Prediction Method of Gene-Disease Associations Based on Optimal Transport for Identifying Genes Related to Immune-Related Adverse Events

Ruhao Liu, Suixue Wang, Hang Yu, Peng Li, Qingchen Zhang

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 5594-5602. DOI: [10.24963/ijcai.2026/623](https://doi.org/10.24963/ijcai.2026/623)

## Abstract

Immune Checkpoint Inhibitors (ICIs) represent a cornerstone of modern cancer immunotherapy. However, their clinical application is frequently accompanied by immune-related Adverse Events (irAEs) of diverse severity. Predicting Gene-Disease Associations (GDAs) is crucial for identifying the related genes that cause irAEs but remains experimentally expensive in the context of cancer immunotherapy. While existing graph neural network-based methods provide solutions to this problem, they often overlook disease-disease and disease-gene associations that may exist but have not yet been identified, leading to contaminated representations. To address these limitations, we propose Gene-Disease Associations based on Optimal Transport (GDAs-OT), a novel framework for GDA prediction. GDAs-OT constructs gene-gene, disease-disease and gene-disease relationships as graphs. Optimal transport was utilized to identify potential disease-disease associations to expand the disease-disease graph structure, and select high-confidence negative pairs in the gene-disease graph. By mapping nodes into a refined embedding space, the processed gene-disease graph guides node representation learning, providing robust node representations. Comprehensive experiments demonstrate that GDAs-OT outperforms state-of-the-art methods across most evaluation metrics. In addition, GDAs-OT successfully identifies potential risk genes for irAEs, providing a computational foundation for understanding the mechanisms of immunotherapy toxicity. Code is available at
https://github.com/RuhaoLiu/GDAs-OT.