Protein interaction networks provide a powerful framework for identifying genes causal for complex genetic diseases. Here, we introduce a general framework, uKIN, that uses prior knowledge of disease-associated genes to guide, within known protein-protein interaction networks, random walks that are initiated from newly identified candidate genes. In large-scale testing across 24 cancer types, we demonstrate that our network propagation approach for integrating both prior and new information not only better identifies cancer driver genes than using either source of information alone but also readily outperforms other state-of-the-art network-based approaches. We also apply our approach to genome-wide association data to identify genes functionally relevant for several complex diseases. Overall, our work suggests that guided network propagation approaches that utilize both prior and new data are a powerful means to identify disease genes. uKIN is freely available for download at: https://github.com/Singh-Lab/uKIN.

蛋白质相互作用网络为识别复杂遗传疾病的基因提供了强大的框架。在这里，我们介绍了一个通用框架 uKIN，它使用疾病相关基因的先验知识来指导，在已知的蛋白质-蛋白质相互作用网络内，从新识别的候选基因启动随机游走。在针对 24 种癌症类型的大规模测试中，我们证明了我们整合先前信息和新信息的网络传播方法不仅比单独使用任一信息源更好地识别癌症驱动基因，而且还轻松优于其他最先进的技术基于网络的方法。我们还将我们的方法应用于全基因组关联数据，以识别与几种复杂疾病功能相关的基因。总的来说，我们的工作表明，利用先前数据和新数据的引导网络传播方法是识别疾病基因的有力手段。 uKIN 可免费下载：https://github.com/Singh-Lab/uKIN。