Background: Today, there are various theories about the causes of hereditary diseases, but doctors believe that both genetic and environmental factors play an essential role in the incidence and spread of these diseases.

Objective: In order to identify genes that are cause the disease, inter-cell or inter-tissue communications must be determined. The inter-cells or inter-tissues interaction could be illustrated by applying the gene expression. The disorders that have led to widespread changes could be identified by investigating gene expression information.

Methods: In this paper, identifying inter-cell and inter-tissue communications for various diseases has been accomplished utilizing an innovative similarity criterion of the graph topological structure characteristics and an extended clustering ensemble. The proposed method is performed in two stages: first, several clustering models have been combined to detect initial inter-cell or inter-tissue communications and produce better results than singular algorithms. Second, the cell-to-cell or tissue-totissue similarity in each cluster is identified through a similarity criterion based on the graph topological structure.

Results: The evaluation of the proposed method has been carried out, benefiting the UCI and FANTOM5 datasets. The results of experiments over FANTOM5 dataset report that the Silhouette coefficient equals 0.901 in 18 clusters for cells and equal to 0.762 in 13 clusters for tissues.

Conclusion: The maximum inter-cells or inter-tissues similarity in each cluster can be exploited to detect the relationships between diseases.

背景：今天，关于遗传性疾病的成因有多种理论，但医生认为遗传和环境因素在这些疾病的发生和传播中起着至关重要的作用。

目的：为了鉴定导致疾病的基因，必须确定细胞间或组织间的通讯。细胞间或组织间的相互作用可以通过应用基因表达来说明。可以通过调查基因表达信息来确定导致广泛变化的疾病。

方法：在本文中，利用图拓扑结构特征的创新相似性标准和扩展聚类集成，已完成识别各种疾病的细胞间和组织间通信。所提出的方法分两个阶段执行：首先，已经组合了几个聚类模型来检测初始细胞间或组织间通信，并产生比单一算法更好的结果。其次，通过基于图拓扑结构的相似性标准识别每个簇中的细胞到细胞或组织到组织的相似性。

结果：对所提出的方法进行了评估，使 UCI 和 FANTOM5 数据集受益。FANTOM5 数据集的实验结果表明，Silhouette 系数在 18 个细胞簇中等于 0.901，在 13 个组织簇中等于 0.762。

结论：可以利用每个簇中的最大细胞间或组织间相似性来检测疾病之间的关系。