Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph
Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph
复制标题
二分图中大规模 miRNA-lncRNA 相互作用网络的新链接预测
DOI：
10.1186/s12920-018-0429-8
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发表时间：
2018-12-31
影响因子：
2.7
通讯作者：
Sun, Yiwen
中科院分区：
文献类型：
--
作者：
Huang, Zhi-An;Huang, Yu-An;Sun, Yiwen
背景目前关于miRNA-lncRNA相互作用的知识和数据仍然有限，预测miRNAs的靶LncRNA的努力很少。越来越多的证据表明，lncRNAs和miRNAs之间的相互作用模式与相对表达水平密切相关，形成了滴定机制。这为特征特征的提取提供了一种有效的方法。此外，利用编码非编码共表达网络和序列数据也有助于衡量miRNAs和lncRNAs之间的相似性。通过对这些相似性的数学分析，我们得到了两个发现：(I)lncRNAs/miRNAs倾向于与表达谱相似的miRNAs/lncRNAs相互作用，反之亦然；(Ii)与一组共同靶基因相互作用的miRNAs倾向于共同靶向共同的lncRNAs。方法在已知的miRNA-lncRNA相互作用网络的基础上，我们提出了一种新的群体偏好贝叶斯协同过滤模型GBCF，用于为单个miRNA或lncRNA提取最高k的概率排序表。经1次剔除、2次交叉验证、5次交叉验证和10次交叉验证，其ROC曲线下面积分别为0.9193、0.8354+/− 0.0079、0.8615+/− 0.0078和0.8928+/− 0.0082，证明了该方法的可靠性和稳健性。结论该方法可用于筛选特定miRNAs潜在的InncRNA靶点，为CERNA调控网络的进一步研究提供参考。
BackgroundCurrent knowledge and data on miRNA-lncRNA interactions is still limited and little effort has been made to predict target lncRNAs of miRNAs. Accumulating evidences suggest that the interaction patterns between lncRNAs and miRNAs are closely related to relative expression level, forming a titration mechanism. It could provide an effective approach for characteristic feature extraction. In addition, using the coding non-coding co-expression network and sequence data could also help to measure the similarities among miRNAs and lncRNAs. By mathematically analyzing these types of similarities, we come up with two findings that (i) lncRNAs/miRNAs tend to collaboratively interact with miRNAs/lncRNAs of similar expression profiles, and vice versa, and (ii) those miRNAs interacting with a cluster of common target genes tend to jointly target at the common lncRNAs.MethodsIn this work, we developed a novel group preference Bayesian collaborative filtering model called GBCF for picking up a top-kprobability ranking list for an individual miRNA or lncRNA based on the known miRNA-lncRNA interaction network.ResultsTo evaluate the effectiveness of GBCF, leave-one-out andk-fold cross validations as well as a series of comparison experiments were carried out. GBCF achieved the values of area under ROC curve of 0.9193, 0.8354+/− 0.0079, 0.8615+/− 0.0078, and 0.8928+/− 0.0082 based on leave-one-out, 2-fold, 5-fold, and 10-fold cross validations respectively, demonstrating its reliability and robustness.ConclusionsGBCF could be used to select potential lncRNA targets of specific miRNAs and offer great insights for further researches on ceRNA regulation network.