One important challenge in the post-genomic era is to explore disease mechanisms by efficiently integrating different types of biological data. In fact, a single disease is usually caused through multiple genes products such as protein complexes rather than single gene. Therefore, it is meaningful for us to discover protein communities from the protein–protein interaction network and use them for inferring disease–disease associations. In this article, we propose a new framework including protein–protein networks, disease–gene associations and disease–complex pairs to cluster protein complexes and infer disease associations. Complexes discovered by our approach is superior in quality (Sn, PPV and ACC) and clustering quantity than other four popular methods on three PPI networks. A systematic analysis shows that disease pairs sharing more protein complexes (such as Glucose and Lipid Metabolic Disorders) are more similar and overlapping proteins may have different roles in different diseases. These findings can provide clinical scholars and medical practitioners with new ideas on disease identification and treatment.

后基因组时代的一项重要挑战是通过有效整合不同类型的生物数据来探索疾病机制。事实上，单一疾病通常是由多个基因产物引起的，例如蛋白质复合物，而不是单个基因。因此，从蛋白质-蛋白质相互作用网络中发现蛋白质群落并将其用于推断疾病-疾病关联对我们来说是有意义的。在本文中，我们提出了一个新的框架，包括蛋白质-蛋白质网络、疾病-基因关联和疾病-复合物对，以聚类蛋白质复合物并推断疾病关联。在三个 PPI 网络上，我们的方法发现的复合物在质量（Sn、PPV 和 ACC）和聚类数量上优于其他四种流行的方法。系统分析表明，共享更多蛋白质复合物（如葡萄糖和脂质代谢紊乱）的疾病对更相似，重叠蛋白质可能在不同疾病中发挥不同的作用。这些发现可以为临床学者和医疗从业者提供疾病识别和治疗的新思路。