Xiao Qiu, Luo Jiawei, Liang Cheng, Cai Jie, Ding Pingjian
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
College of Information Science and Engineering, Shandong Normal University, Jinan, China.
Bioinformatics. 2018 Jan 15;34(2):239-248. doi: 10.1093/bioinformatics/btx545.
MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.
In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs.
The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/.
Supplementary data are available at Bioinformatics online.
微小RNA（miRNA）在转录后调控和各种细胞过程中发挥着关键作用。疾病相关miRNA的识别为系统层面疾病的潜在发病机制提供了深刻见解。然而，大多数现有的计算方法偏向于已知的miRNA-疾病关联，这对于那些没有任何已知关联信息的新疾病或miRNA并不适用。
在本研究中，我们提出了一种在异质组学数据中使用图正则化非负矩阵分解的新方法，称为GRNMF，以发现miRNA与疾病之间的潜在关联，特别是对于新疾病和miRNA，或那些已知关联稀疏的疾病和miRNA。首先，我们整合疾病语义信息和miRNA功能信息，分别估计疾病相似性和miRNA相似性。考虑到新疾病或miRNA没有可用的相互作用观测值，我们开发了一个预处理步骤来构建相互作用得分概况，这将有助于预测。接下来，利用图正则化非负矩阵分解框架同时识别所有疾病的潜在关联。结果表明，与其他近期方法相比，我们提出的方法能够以更高的准确性有效地对疾病相关的miRNA进行优先级排序。此外，案例研究也证明了GRNMF在推断那些新疾病和miRNA的未知miRNA-疾病关联方面的有效性。
GRNMF的代码可在https://github.com/XIAO-HN/GRNMF/上免费获取。
补充数据可在《生物信息学》在线获取。
