ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction
ELLPMDA: Ensemble learning and link prediction for miRNA-disease association prediction
复制标题
用于 miRNA 疾病关联预测的 ELLPMDA 集成学习和链接预测
DOI：
10.1080/15476286.2018.1460016
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发表时间：
2018-01-01
期刊：
影响因子：
4.1
通讯作者：
Zhao, Yan
中科院分区：
文献类型：
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作者：
Chen, Xing;Zhou, Zhihan;Zhao, Yan
近年来，越来越多的证据表明，miRNA在人类各种复杂疾病的进展和发展中起着至关重要的作用，这表明，确定miRNA与疾病的关联可以使我们在miRNA水平上了解疾病。因此，揭示更多潜在的mirna与疾病的关联是生物医学领域的重要课题。然而，如果我们检查所有可能的mirna -疾病对，将是非常昂贵和耗时的。因此，人们迫切需要更准确、更有效的方法来检测潜在的mirna与疾病的关联。在本研究中，我们开发了一个集成学习和链接预测mirna -疾病关联预测（ELLPMDA）的计算模型来实现这一目标。通过整合miRNA与疾病的功能相似度、疾病语义相似度、miRNA-疾病关联和高斯谱核相似度，构建了相似度网络，并利用集成学习对三种经典相似度算法给出的排序结果进行组合。为了评估ELLPMDA的性能，我们利用了全局和局部留一交叉验证（LOOCV）、五重交叉验证（CV）和三种案例研究。结果表明，ELLPMDA在全局、局部和5倍CV上的auc分别为0.9181、0.8181和0.9193+/-0.0002，显著优于以往的几乎所有方法。此外，在肾肿瘤、淋巴瘤、前列腺肿瘤、结肠肿瘤和食管肿瘤的三种不同类型的病例研究中，前50个预测mirna中的88%、92%、86%、98%和98%分别得到了证实。此外，ELLPMDA基于全局相似性度量，适用于没有已知相关mirna的新疾病。
Recently, accumulating evidences have indicated miRNAs play critical roles in the progression and development of various human complex diseases, which pointed out that identifying miRNA-disease association could enable us to understand diseases at miRNA level. Thus, revealing more and more potential miRNA-disease associations is a vital topic in biomedical domain. However, it will be extremely expensive and time-consuming if we examine all the possible miRNA-disease pairs. Therefore, more accurate and efficient methods are being highly requested to detect potential miRNA-disease associations. In this study, we developed a computational model of Ensemble Learning and Link Prediction for miRNA-Disease Association prediction (ELLPMDA) to achieve this goal. By integrating miRNA functional similarity, disease semantic similarity, miRNA-disease association and Gaussian profile kernel similarity for miRNAs and diseases, we constructed a similarity network and utilized ensemble learning to combine rank results given by three classic similarity-based algorithms. To evaluate the performance of ELLPMDA, we exploited global and local Leave-One-Out Cross Validation (LOOCV), 5-fold Cross Validation (CV) and three kinds of case studies. As a result, the AUCs of ELLPMDA is 0.9181, 0.8181 and 0.9193+/-0.0002 in global LOOCV, local LOOCV and 5-fold CV, respectively, which significantly exceed almost all the previous methods. Moreover, in three distinct kinds of case studies for Kidney Neoplasms, Lymphoma, Prostate Neoplasms, Colon Neoplasms and Esophageal Neoplasms, 88%, 92%, 86%, 98% and 98% out of the top 50 predicted miRNAs has been confirmed, respectively. Besides, ELLPMDA is based on global similarity measure and applicable to new diseases without any known related miRNAs.