Lin Yong, Ma Xiaoke
School of Physics and Electronic Information Engineering, Ningxia Normal University, Guyuan, China.
School of Computer Science and Technology, Xidian University, Xi'an, China.
Front Genet. 2021 Jan 12;11:622234. doi: 10.3389/fgene.2020.622234. eCollection 2020.
Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is both costly and time-consuming. Therefore, it is necessary to design computational approaches to predict the disease-lincRNA associations that shed light on the mechanisms of diseases. In this study, we develop a co-regularized non-negative matrix factorization (aka ) to identify potential disease-lincRNA associations by integrating the gene expression of lincRNAs, genetic interaction network for mRNA genes, gene-lincRNA associations, and disease-gene associations. The Cr-NMF algorithm factorizes the disease-lincRNA associations, while the other associations/interactions are integrated using regularization. Furthermore, the regularization does not only preserve the topological structure of the lincRNA co-expression network, but also maintains the links "lincRNA → gene → disease." Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy on predicting the disease-lincRNA associations. The model and algorithm provide an effective way to explore disease-lncRNA associations.
长链基因间非编码核糖核酸（lincRNAs）是许多复杂疾病的关键调节因子，而鉴定疾病与lincRNA的关联既昂贵又耗时。因此，有必要设计计算方法来预测疾病与lincRNA的关联，从而揭示疾病的发病机制。在本研究中，我们开发了一种共正则化非负矩阵分解（即Cr-NMF）方法，通过整合lincRNAs的基因表达、mRNA基因的遗传相互作用网络、基因与lincRNA的关联以及疾病与基因的关联，来识别潜在的疾病与lincRNA的关联。Cr-NMF算法对疾病与lincRNA的关联进行分解，而其他关联/相互作用则通过正则化进行整合。此外，正则化不仅保留了lincRNA共表达网络的拓扑结构，还维持了“lincRNA→基因→疾病”的联系。实验结果表明，在预测疾病与lincRNA的关联方面，所提出的算法在准确性上优于现有方法。该模型和算法为探索疾病与lncRNA的关联提供了一种有效方法。
