Studying the similarity of diseases can help us to explore the pathological characteristics of complex diseases, and help provide reliable reference information for inferring the relationship between new diseases and known diseases, so as to develop effective treatment plans. To obtain the similarity of the disease, most previous methods either use a single similarity metric such as semantic score, functional score from single data source, or utilize weighting coefficients to simply combine multiple metrics with different dimensions. In this paper, we proposes a method to predict the similarity of diseases by node representation learning. We first integrate the semantic score and topological score between diseases by combining multiple data sources. Then for each disease, its integrated scores with all other diseases are utilized to map it into a vector of the same spatial dimension, and the vectors are used to measure and comprehensively analyze the similarity between diseases. Lastly, we conduct comparative experiment based on benchmark set and other disease nodes outside the benchmark set. Using the statistics such as average, variance, and coefficient of variation in the benchmark set to evaluate multiple methods demonstrates the effectiveness of our approach in the prediction of similar diseases.

中文翻译：

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异构疾病信息网络的类似疾病预测。

研究疾病的相似性可以帮助我们探索复杂疾病的病理特征，并为推断新疾病与已知疾病之间的关系提供可靠的参考信息，从而制定有效的治疗方案。为了获得疾病的相似性，大多数以前的方法要么使用单个相似性度量（例如语义评分，来自单个数据源的功能评分），要么利用加权系数来简单地将具有不同维度的多个度量组合在一起。在本文中，我们提出了一种通过节点表示学习来预测疾病相似性的方法。我们首先通过组合多个数据源来整合疾病之间的语义评分和拓扑评分。然后针对每种疾病 利用其与所有其他疾病的综合得分将其映射到相同空间维度的向量中，并使用这些向量来测量和全面分析疾病之间的相似性。最后，我们根据基准集和基准集以外的其他疾病节点进行比较实验。使用基准集中的平均值，方差和变异系数等统计数据来评估多种方法，证明了我们的方法在预测相似疾病中的有效性。