Prediction of circRNA and disease association based on fusion similarity and tripartite graph

doi:10.3785/j.issn.1008-973X.2023.12.014

Journal of ZheJiang University (Engineering Science)

2023, Vol. 57

Issue (12): 2467-2475 DOI: 10.3785/j.issn.1008-973X.2023.12.014

Prediction of circRNA and disease association based on fusion similarity and tripartite graph

Bo WANG(

),Ting-bin LIU,Jian-fei ZHANG,Xiao-xin DU,Xin-wei WANG

College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, China

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Abstract

Traditional biomedical experimental methods for verifying the relationship between circRNA and disease are time-consuming, laborious, and costly. Therefore, a model called FSTPGCDA was proposed for circRNA-disease association prediction research, which was based on the fusion of tripartite graph and fusion similarity. FSTPGCDA incorporated circRNA-disease association information, circRNA-gene association information, circRNA sequence information, and disease semantic information. The similarity matrix was obtained by combining Laplacian eigenmaps and Jaccard index for fusion similarity calculation. The fusion similarity matrix was generated by weighting the similarity matrices obtained from different similarity algorithms. The gene-circRNA-disease tripartite graph was constructed using the circRNA-disease association matrix and circRNA-gene association matrix. The initial resource allocation was performed using the fusion similarity, and resource allocation was carried out using a greedy algorithm. The experimental validation demonstrated that FSTPGCDA exhibited good predictive performance and robustness.

Key words： circRNA and disease association multi-source information fusion similarity fusion tripartite graph case validation

Received: 14 May 2023 Published: 27 December 2023

CLC:

TP 393

Fund: 黑龙江省教育厅基本科研业务费面上项目 (145209125)

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Cite this article:

Bo WANG,Ting-bin LIU,Jian-fei ZHANG,Xiao-xin DU,Xin-wei WANG. Prediction of circRNA and disease association based on fusion similarity and tripartite graph. Journal of ZheJiang University (Engineering Science), 2023, 57(12): 2467-2475.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.12.014 OR https://www.zjujournals.com/eng/Y2023/V57/I12/2467

基于融合相似性和三部图的 circRNA 与疾病关联预测

传统的生物医学实验方法验证circRNA与疾病之间的关系存在耗时、耗力且成本过高的问题，为此提出基于三部图融合相似性的circRNA与疾病关联预测研究的模型（FSTPGCDA）. FSTPGCDA引入circRNA-disease关联信息、circRNA-gene关联信息、circRNA序列信息和疾病语义信息. 进行拉普拉斯特征映射和Jaccard指标的融合相似性计算得到相似性矩阵，将不同相似性算法得到的相似性矩阵加权融合得到融合相似性矩阵. 利用circRNA-disease关联矩阵和circRNA-gene关联矩阵构建gene-circRNA-disease三部图. 通过融合相似性方法为三部图分配初始资源，使用贪心算法进行资源分配. 实例验证表明，FSTPGCDA的预测性能和鲁棒性较好.

关键词： circRNA与疾病关联, 多源信息融合, 相似性融合, 三部图, 实例验证

Tab.1 Data set association information

Fig.1 Flow chart of circRNA and disease association prediction model based on fusion similarity and tripartite graph

Tab.2 Comparison of indicators for proposed model at different test methods

Fig.2 Comparison of ROC for different models

Fig.3 Comparison of PR for different models

Fig.4 ROC comparison of different similarity for proposed model

Tab.3 First 15 circRNAs associated with bladder cancer

Fig.5 Survival analysis of CDR1 gene in patients with gastric cancer

Fig.6 Differentiation and expression of CDR1 gene in normal and tumor sample

Fig.7 Gene set enriched in immune deficiency


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