BACKGROUND A collection of disease-associated data contributes to study the association between diseases. Discovering closely related diseases plays a crucial role in revealing their common pathogenic mechanisms. This might further imply treatment that can be appropriated from one disease to another. During the past decades, a number of approaches for calculating disease similarity have been developed. However, most of them are designed to take advantage of single or few data sources, which results in their low accuracy. METHODS In this paper, we propose a novel method, called MultiSourcDSim, to calculate disease similarity by integrating multiple data sources, namely, gene-disease associations, GO biological process-disease associations and symptom-disease associations. Firstly, we establish three disease similarity networks according to the three disease-related data sources respectively. Secondly, the representation of each node is obtained by integrating the three small disease similarity networks. In the end, the learned representations are applied to calculate the similarity between diseases. RESULTS Our approach shows the best performance compared to the other three popular methods. Besides, the similarity network built by MultiSourcDSim suggests that our method can also uncover the latent relationships between diseases. CONCLUSIONS MultiSourcDSim is an efficient approach to predict similarity between diseases.

中文翻译：

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MultiSourcDSim：探索疾病相似性的综合方法。

背景技术疾病相关数据的收集有助于研究疾病之间的关联。发现密切相关的疾病对于揭示其共同的致病机制起着至关重要的作用。这可能进一步意味着可以将一种疾病适用于另一种疾病的治疗。在过去的几十年中，已经开发了许多计算疾病相似性的方法。然而，它们大多数都是为了利用单一或少数数据源而设计的，这导致其准确性较低。方法在本文中，我们提出了一种称为MultiSourcDSim的新方法，通过整合多个数据源（即基因-疾病关联、GO生物过程-疾病关联和症状-疾病关联）来计算疾病相似性。首先，我们分别根据三个疾病相关数据源建立三个疾病相似网络。其次，通过整合三个小的疾病相似网络来获得每个节点的表示。最后，应用学习到的表示来计算疾病之间的相似性。结果与其他三种流行方法相比，我们的方法显示出最佳性能。此外，MultiSourcDSim 构建的相似性网络表明我们的方法还可以揭示疾病之间的潜在关系。结论 MultiSourcDSim 是预测疾病之间相似性的有效方法。