Supervised Graph Contrastive Learning for Gene Regulatory Networks

• URL: http://arxiv.org/abs/2505.17786v4
• Date: Thu, 25 Sep 2025 14:44:32 GMT
• Title: Supervised Graph Contrastive Learning for Gene Regulatory Networks
• Authors: Sho Oshima, Yuji Okamoto, Taisei Tosaki, Ryosuke Kojima, Yasushi Okuno,
• Abstract summary: Graph Contrastive Learning (GCL) is a powerful self-supervised learning framework that performs data augmentation through graph perturbations.<n>The artificial perturbations commonly used in GCL, such as node dropping, induce structural changes that can diverge from biological reality.<n>We propose SupGCL, a new GCL method that directly incorporates biological perturbations from gene knockdown experiments as supervision.
• Score: 5.0455825968650725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 to be avoided. However, this trend overlooks the fundamental insight that structural changes from biologically meaningful perturbations are not a problem to be avoided but 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 experiments as supervision. SupGCL is a probabilistic formulation that continuously generalizes conventional GCL, linking artificial augmentations with real perturbations measured in knockdown experiments and using the latter as explicit supervisory signals. To assess effectiveness, we train GRN representations with SupGCL and evaluate their performance on downstream tasks. The evaluation includes both node-level tasks, such as gene function classification, and graph-level tasks on patient-specific GRNs, such as patient survival hazard prediction. Across 13 tasks built from GRN datasets derived from patients with three cancer types, SupGCL consistently outperforms state-of-the-art baselines.

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