{"type":"ScholarlyArticle","title":"GDAs-OT: A Prediction Method of Gene-Disease Associations Based on Optimal Transport for Identifying Genes Related to Immune-Related Adverse Events","authors":[{"name":"Ruhao Liu","slug":"ruhao-liu","given":"Ruhao","family":"Liu","affiliation":""},{"name":"Suixue Wang","slug":"suixue-wang","given":"Suixue","family":"Wang","affiliation":""},{"name":"Hang Yu","slug":"hang-yu","given":"Hang","family":"Yu","affiliation":""},{"name":"Peng Li","slug":"peng-li","given":"Peng","family":"Li","affiliation":""},{"name":"Qingchen Zhang","slug":"qingchen-zhang","given":"Qingchen","family":"Zhang","affiliation":""}],"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\r\nhttps://github.com/RuhaoLiu/GDAs-OT.","doi":"10.24963/ijcai.2026/623","url":"https://ijcai.org/proceedings/2026/623","pdf":"https://ijcai.org/proceedings/2026/0623.pdf","markdown":"https://ijcai.org/proceedings/2026/623.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/623","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":623,"pages":{"first":5594,"last":5602},"keywords":["Multidisciplinary Topics and Applications: Bioinformatics","Multidisciplinary Topics and Applications: Life sciences","Multidisciplinary Topics and Applications: Health and medicine"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}