MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.
MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.
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MKRMDA：基于多核学习的 Kronecker 正则化最小二乘法，用于 miRNA 疾病关联预测
DOI：
10.1186/s12967-017-1340-3
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发表时间：
2017-12-12
影响因子：
7.4
通讯作者：
Yan GY
中科院分区：
文献类型：
--
作者：
Chen X;Niu YW;Wang GH;Yan GY
近年来，随着对microRNA（miRNA）研究的不断深入，大量实验证据表明miRNA可能与人类多种复杂疾病的发生、发展有关。因此，开展预测疾病相关miRNAs的相关研究，对人类疾病的有效预防、诊断和治疗具有重要意义。特别是构建预测miRNA与疾病关系的计算方法，其可行性和有效性值得进一步研究。在这项工作中，我们开发了一种新的基于多核学习的Kronecker正则化最小二乘计算模型用于miRNA-疾病关联预测（MKRMDA），该模型可以通过自动优化疾病和miRNA的多个核的组合来揭示潜在的miRNA-疾病关联。MKRMDA在全局和局部留一法交叉验证中获得的AUC分别为0.9040和0.8446。同时，在五重交叉验证中，MKRMDA的平均AUC为0.8894 ± 0.0015。此外，我们进行了三种不同类型的案例研究，对一些重要的人类癌症的进一步性能评估。在基于HMDDv2.0数据库中已知的miRNA与疾病相关性的结肠癌、食道癌和淋巴瘤案例研究中，相应的前50种预测miRNA中分别有76%、94%和88%得到了实验报告的证实。在HMDDv1.0数据库中没有任何已知相关miRNA的新疾病和仅具有已知关联的疾病的另外两种病例研究中，两种不同癌症的验证比率分别为88%和94%。上述结果充分说明了MKRMDA的可靠预测能力。我们期望MKRMDA可以促进该领域的进一步发展和生物医学研究人员的后续调查。本文的在线版本（10.1186/s12967-017-1340-3）包含补充材料，可供授权用户使用。
Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA–disease associations is worthy of more studies because of the feasibility and effectivity. In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA–disease association prediction (MKRMDA), which could reveal potential miRNA–disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA–disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers. The online version of this article (10.1186/s12967-017-1340-3) contains supplementary material, which is available to authorized users.
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