A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction
A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction
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一种基于超级疾病和 miRNA 的新型计算模型，用于潜在 miRNA-疾病关联预测
DOI：
10.1039/c6mb00853d
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发表时间：
2017-06-01
影响因子：
--
通讯作者：
You, Zhu-Hong
中科院分区：
文献类型：
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作者：
Chen, Xing;Jiang, Zhi-Chao;You, Zhu-Hong
近年来，越来越多的研究表明，microRNAs （miRNAs）在各种复杂的人类疾病中起着至关重要的作用，可以作为早期癌症检测的重要生物标志物。开发预测mirna与疾病潜在关联的计算模型已成为显著减少实验时间和成本的研究热点。考虑到以往计算模型的种种缺点，我们提出了一种基于超级疾病和miRNA的潜在miRNA-疾病关联预测计算模型（SDMMDA），通过整合已知关联、疾病语义相似度、miRNA功能相似度以及疾病和miRNA的高斯相互作用谱核相似度来预测潜在的miRNA-疾病关联。SDMMDA可以应用于没有任何已知相关mirna的新疾病，也可以应用于没有任何已知相关疾病的新mirna。由于已知的mirna -疾病关联非常少，并且在已知的训练数据集中“缺失”了许多关联，因此我们引入了“超级mirna”和“超级疾病”的概念，以增强疾病和mirna的相似性度量。这些超类可以帮助包括缺失的关联并提高预测的准确性。结果表明，SDMMDA在全局留一差交叉验证、局部留一差交叉验证和5倍交叉验证中的auc分别为0.9032、0.8323和0.8970，取得了可靠的性能。此外，我们将食道肿瘤、乳腺肿瘤和前列腺肿瘤作为独立的案例研究，在最近的实验文献中，预测前50个mirna中有46个、43个和48个被成功证实。预计SDMMDA将成为指导实验的重要生物资源。
In recent years, more and more studies have indicated that microRNAs (miRNAs) play critical roles in various complex human diseases and could be regarded as important biomarkers for cancer detection in early stages. Developing computational models to predict potential miRNA-disease associations has become a research hotspot for significant reduction of experimental time and cost. Considering the various disadvantages of previous computational models, we proposed a novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction (SDMMDA) to predict potential miRNA-disease associations by integrating known associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs. SDMMDA could be applied to new diseases without any known associated miRNAs as well as new miRNAs without any known associated diseases. Due to the fact that there are very few known miRNA-disease associations and many associations are 'missing' in the known training dataset, we introduce the concepts of 'super-miRNA' and 'super-disease' to enhance the similarity measures of diseases and miRNAs. These super classes could help in including the missing associations and improving prediction accuracy. As a result, SDMMDA achieved reliable performance with AUCs of 0.9032, 0.8323, and 0.8970 in global leave-one-out cross validation, local leave-one-out cross validation, and 5-fold cross validation, respectively. In addition, esophageal neoplasms, breast neoplasms, and prostate neoplasms were taken as independent case studies, where 46, 43 and 48 out of the top 50 predicted miRNAs were successfully confirmed by recent experimental literature. It is anticipated that SDMMDA would be an important biological resource for experimental guidance.