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[2505.17786] Supervised Graph Contrastive Learning for Gene Regulatory Networks
Summary
This article presents SupGCL, a novel supervised graph contrastive learning method for gene regulatory networks, leveraging biological perturbations for improved representation learning and downstream task performance.
Why It Matters
The research addresses a critical gap in graph representation learning by integrating biologically relevant perturbations, enhancing the understanding of gene regulatory networks. This approach could lead to significant advancements in cancer research and personalized medicine, making it highly relevant for both academic and clinical applications.
Key Takeaways
- SupGCL incorporates biological perturbations from gene knockdown experiments as supervision.
- The method enhances disease-subtype clustering and improves performance on 13 downstream tasks.
- SupGCL offers a probabilistic framework that links artificial augmentations with real biological data.
Computer Science > Machine Learning arXiv:2505.17786 (cs) [Submitted on 23 May 2025 (v1), last revised 19 Feb 2026 (this version, v5)] Title:Supervised Graph Contrastive Learning for Gene Regulatory Networks Authors:Sho Oshima, Yuji Okamoto, Taisei Tosaki, Ryosuke Kojima View a PDF of the paper titled Supervised Graph Contrastive Learning for Gene Regulatory Networks, by Sho Oshima and 3 other authors View PDF HTML (experimental) Abstract:Graph Contrastive Learning (GCL) is a powerful self-supervised learning framework that performs data augmentation through graph perturbations, with growing applications in the analysis of biological networks such as Gene Regulatory Networks (GRNs). The artificial perturbations commonly used in GCL, such as node dropping, induce structural changes that can diverge from biological reality. This concern has contributed to a broader trend in graph representation learning toward augmentation-free methods, which view such structural changes as problematic and should be avoided. However, this trend overlooks the fundamental insight that structural changes from biologically meaningful perturbations are not a problem to be avoided, but rather a rich source of information, thereby ignoring the valuable opportunity to leverage data from real biological experiments. Motivated by this insight, we propose SupGCL (Supervised Graph Contrastive Learning), a new GCL method for GRNs that directly incorporates biological perturbations from gene knockdown exp...
