基于信任网络的多中心衰老时钟预测联邦学习框架

中文摘要 AI 辅助

衰老时钟量化生物衰老过程，并有助于表征个体健康状况。哪些蛋白质相互作用对准确的衰老时钟至关重要，它们是零阶还是高阶相互作用？解决这些问题需要从分布在多个医疗中心的大型分子数据集中学习，而隐私约束阻止了集中式数据共享。联邦学习提供了一种自然的解决方案，但在此场景下面临四个挑战：本地样本量有限、中心间信任稀疏且有方向性、需要在支持可解释性的同时保持判别性年龄预测能力，以及异构跨中心数据下的模型漂移和遗忘。我们提出了TNFL，一种基于信任网络的联邦学习框架，该框架沿有向成对信任关系逐步传播模型，无需集中聚合。TNFL结合了年龄感知的专家混合模型与生成式回放，以保留先前学习的信息并减少遗忘和漂移。在多个分子数据集上的实验表明，TNFL能够在有限本地数据下实现有效的衰老时钟预测，提供可解释的年龄依赖性预测模式，并在不同交互阶数下保持稳定性能。为了探究生物学问题，我们通过功能与网络分析分析了TNFL识别的成对蛋白质相互作用及其高阶组织。识别出的相互作用反复形成跨越多个衰老相关生物系统的协调高阶子网络，其中多个蛋白质在子网络间重复出现。这些发现表明，TNFL捕获了超越孤立成对关联的分子关系，并揭示了与衰老相关的连贯高阶生物组织。

英文摘要

Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order or higher-order? Addressing these questions requires learning from large molecular datasets distributed across medical centers, where privacy constraints prevent centralized data sharing. Federated learning offers a natural solution but faces four challenges in this setting: limited local sample sizes, sparse and directional inter-center trust, the need to retain discriminative age prediction while supporting interpretation, and model drift and forgetting under heterogeneous cross-center data. We propose TNFL, a trust-network-based federated learning framework that progressively propagates models along directed pairwise trust relations without centralized aggregation. TNFL combines an age-aware mixture-of-experts model with generative replay to preserve previously learned information and reduce forgetting and drift. Experiments across multiple molecular datasets show that TNFL enables effective aging-clock prediction with limited local data, provides interpretable age-dependent prediction patterns, and maintains stable performance across interaction orders. To investigate the biological questions, we analyze TNFL-identified pairwise protein interactions and their higher-order organization through functional and network analyses. The identified interactions repeatedly form coordinated higher-order subnetworks spanning multiple aging-related biological systems, with several proteins recurring across subnetworks. These findings suggest that TNFL captures molecular relationships beyond isolated pairwise associations and reveals coherent higher-order biological organization associated with aging.

发表机构

• PolyU Academy for Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学人工智能研究院）
• Quantum Life
• Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）
• Kyoto University（京都大学）
• Jilin University（吉林大学）

机构由 AI 辅助整理，请以论文原文为准。