基于融合相似性和三部图的 circRNA 与疾病关联预测

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

浙江大学学报(工学版)

2023, Vol. 57

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

计算机技术

基于融合相似性和三部图的 circRNA 与疾病关联预测

王波(

),刘庭斌,张剑飞,杜晓昕,王鑫炜

齐齐哈尔大学计算机与控制工程学院，黑龙江齐齐哈尔 161006

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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摘要：

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

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

收稿日期: 2023-05-14 出版日期: 2023-12-27

CLC:

TP 393

基金资助: 黑龙江省教育厅基本科研业务费面上项目 (145209125)

作者简介: 王波（1980—），男，教授，从事大数据分析与挖掘研究. orcid.org/0000-0002-4983-7288. E-mail： bowangdr@qqhru.edu.cn

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引用本文:

王波,刘庭斌,张剑飞,杜晓昕,王鑫炜. 基于融合相似性和三部图的 circRNA 与疾病关联预测[J]. 浙江大学学报(工学版), 2023, 57(12): 2467-2475.

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.

链接本文:

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

表 1 数据集关联信息

图 1 基于融合相似性和三部图的circRNA与疾病关联预测模型流程图

表 2 所提模型在不同测试方法下的评估指标对比

图 2 不同模型的ROC对比

图 3 不同模型的PR对比

图 4 所提模型不同相似性的ROC对比

表 3 前15个与膀胱癌有关联的circRNA

图 5 CDR1基因在胃癌患者的生存分析图

图 6 CDR1基因在正常和肿瘤样本中的分化表达

图 7 免疫缺陷中富集的基因集

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