TOMOC：一种拓扑驱动的多目标进化框架，用于在噪声PPI网络中稳健地检测重叠蛋白质复合物

摘要(中文译文)

蛋白质复合物构成细胞内基本的功能模块，在调控众多生物过程中发挥关键作用。因此，从蛋白质-蛋白质相互作用（PPI）网络中检测蛋白质复合物已成为计算系统生物学的核心问题。然而，许多现有计算方法难以准确识别蛋白质同时参与多个功能模块的重叠复合物。此外，由于实验限制，大规模PPI网络本身存在噪声和不完整性，这显著影响了复合物检测方法的可靠性。在本工作中，我们提出TOMOC，一种拓扑驱动的多目标进化框架，用于在噪声PPI网络中稳健检测重叠蛋白质复合物。TOMOC的新颖性在于将拓扑驱动的双目标公式化建模、天然支持重叠成员关系的基于边的进化表示，以及拓扑感知的结构精化机制集成于一个统一框架中，用于噪声PPI网络中的蛋白质复合物检测。该框架引入基于边的进化表示，在相互作用层面建模候选解，使重叠成员关系在解码过程中自然涌现。它进一步通过最小化平均电导和三角形密度损失来优化两个互补的结构目标，使算法能够平衡边界质量和内部结构密度。此外，设计了拓扑感知的结构重叠精化（SOR）算子，通过边界感知修复、三角形闭合扩展和三角形支持剪枝来提高结构一致性以及对噪声相互作用的鲁棒性。在三个基准PPI网络（Yeast-D1、Yeast-D2和Collins）上进行的大量实验表明，TOMOC在精确率、召回率和F1分数方面与多种最先进方法相比取得了有竞争力的性能。所提出的TOMOC框架提供了一种有效且可扩展的拓扑驱动方法，可直接从PPI网络拓扑中检测重叠蛋白质复合物。通过将多目标进化优化与拓扑感知精化机制相结合，TOMOC有效捕捉了蛋白质复合物的结构特征，并在应用于大规模且含噪声的生物相互作用网络时表现出强鲁棒性。

Abstract

Protein complexes constitute fundamental functional modules within cells and play a crucial role in regulating many biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks has therefore become a central problem in computational systems biology. However, many existing computational approaches struggle to accurately identify overlapping complexes where proteins participate in multiple functional modules simultaneously. In addition, large-scale PPI networks are inherently noisy and incomplete due to experimental limitations, which significantly affects the reliability of complex detection methods. In this work, we propose TOMOC, a topology-driven multi-objective evolutionary framework for robust detection of overlapping protein complexes in noisy PPI networks. The novelty of TOMOC lies in the integration of a topology-driven bi-objective formulation, an edge-based evolutionary representation that naturally supports overlapping memberships, and a topology-aware structural refinement mechanism within a unified framework for protein complex detection in noisy PPI networks. The proposed framework introduces an edge-based evolutionary representation that models candidate solutions at the interaction level, allowing overlapping memberships to emerge naturally during decoding. It further optimizes two complementary structural objectives by minimizing average conductance and triangle-density loss, enabling the algorithm to balance boundary quality and internal structural density. In addition, a topology-aware structural overlap refinement (SOR) operator is designed to improve structural coherence and robustness against noisy interactions through boundary-aware repair, triangle-closure expansion, and triangle-support pruning. Extensive experiments conducted on three benchmark PPI networks (Yeast-D1, Yeast-D2, and Collins) demonstrate that TOMOC achieves competitive performance compared with several state-of-the-art methods in terms of precision, recall, and F1-score. The proposed TOMOC framework provides an effective and scalable topology-driven approach for detecting overlapping protein complexes directly from PPI network topology. By integrating multi-objective evolutionary optimization with topology-aware refinement mechanisms, TOMOC effectively captures the structural characteristics of protein complexes and demonstrates strong robustness when applied to large and noisy biological interaction networks.

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