GIMDA: Graphlet interaction-based MiRNA-disease association prediction.
GIMDA: Graphlet interaction-based MiRNA-disease association prediction.
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GIMDA Graphlet 基于相互作用的 miRNA 与疾病关联预测
DOI：
10.1111/jcmm.13429
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发表时间：
2018-03
影响因子：
5.3
通讯作者：
Yan GY
中科院分区：
文献类型：
--
作者：
Chen X;Guan NN;Li JQ;Yan GY
MicroRNAs（miRNAs）已被许多实验研究证实与人类多种复杂疾病密切相关。因此，建立有效的计算模型来预测miRNAs与疾病之间的关系是非常必要的。在这项工作中，我们提出了一个预测模型的Graphlet相互作用的miRNA-疾病关联预测（GIMDA），通过整合疾病语义相似性，miRNA功能相似性，高斯相互作用轮廓核相似性和实验证实的miRNA-疾病关联。通过测量两种miRNA或两种疾病之间的graphlet相互作用来计算miRNA与疾病的相关评分。GIMDA的新奇之处在于，我们使用小图交互来分析图中两个节点之间的复杂关系。GIMDA在全局和局部留一法交叉验证（LOOCV）中的AUC分别为0.9006和0.8455。五重交叉验证的平均结果达到0.8927 ± 0.0012。在基于HMDD V2.0数据库的结肠肿瘤、肾肿瘤和前列腺肿瘤的病例研究中，通过dbDEMC和miR 2Disease验证了GIMDA预测的前50个潜在miRNA中的45、45、41个。此外，在没有任何已知相关miRNA的新疾病的案例研究和使用HMDD V1.0预测潜在miRNA-疾病关联的案例研究中，实验文献验证的前50种miRNA的百分比也很高。
MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA‐Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA‐disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave‐one‐out cross‐validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five‐fold cross‐validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA‐disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.
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