Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g., disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrative approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established first. Then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.

中文翻译：

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用于检测类似疾病的综合疾病信息网络方法。

疾病相似性分析在发病机制揭示、治疗推荐和致病基因预测方面具有显着影响。先前的工作基于从生物医学本体（例如，疾病本体）或致病分子的功能获得的语义来研究疾病相似性。然而，此类方法几乎集中于获取疾病特征的单一视角，这可能导致类似疾病检测的结果出现偏差。为了解决这个问题，我们提出了一种基于疾病信息网络的综合方法，名为 MISSION 来检测类似疾病。通过利用疾病与其他生物医学实体之间的关联，首先建立疾病信息网络。然后，将从疾病分类、属性、文献和注释等方面提取的疾病相似性特征整合到疾病信息网络中。最后根据综合疾病信息进行top-k相似疾病查询。在真实世界数据集上进行的实验表明，MISSION 在类似疾病检测中是有效且有用的。