监督图对比学习用于基因调控网络
Supervised Graph Contrastive Learning for Gene Regulatory Networks
AI总结：
本文提出SupGCL方法，通过整合基因敲除实验中的生物扰动作为监督信号，改进基因调控网络的表示学习，提升疾病亚型识别和下游任务性能。
摘要：
图对比学习（GCL）是一种强大的自监督学习框架，通过图扰动进行数据增强，在分析生物网络如基因调控网络（GRNs）中应用广泛。GCL中常用的节点删除等人工扰动会引起结构变化，可能偏离生物现实。这一问题促使图表示学习向无增强方法发展，但该趋势忽略了生物有意义的扰动引起的结构变化并非需要避免的问题，而是信息来源。本文提出SupGCL，一种新的GRN GCL方法，直接整合基因敲除实验中的生物扰动作为监督。SupGCL是一种概率模型，连续扩展传统GCL，将人工增强与实测的敲除实验扰动联系起来，并利用后者作为显式监督。在三种癌症类型的患者衍生GRNs上，我们训练GRN表示并评估：（i）嵌入空间分析，产生更清晰的疾病亚型结构并提升聚类；（ii）任务特定微调，其在13个下游任务中一致优于强图表示学习基线，涵盖基因层面的功能注释和患者层面预测。
英文摘要：
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 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 supervision. On patient-derived GRNs from three cancer types, we train GRN representations with SupGCL and evaluate it in two regimes: (i) embedding space analysis, where it yields clearer disease-subtype structure and improves clustering, and (ii) task-specific fine-tuning, where it consistently outperforms strong graph representation learning baselines on 13 downstream tasks spanning gene-level functional annotation and patient-level prediction.
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