Huang Yu-An, Chen Xing, You Zhu-Hong, Huang De-Shuang, Chan Keith C C
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
Oncotarget. 2016 May 3;7(18):25902-14. doi: 10.18632/oncotarget.8296.
Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches.
越来越多的观察结果表明，长链非编码RNA（lncRNAs）在各种关键生物学过程以及多种人类疾病的发生和发展中发挥着重要作用。构建lncRNA功能相似性网络有助于开发用于推断lncRNA功能和识别lncRNA-疾病关联的计算模型。然而，在量化lncRNA功能相似性方面所做的工作很少。在本研究中，我们基于具有相似生物学功能的lncRNAs往往参与相似疾病这一假设，开发了一种改进的lncRNA功能相似性计算模型（ILNCSIM）。主要改进来自于信息内容概念与用于疾病相似性计算的疾病有向无环图的层次结构的结合。ILNCSIM与先前提出的用于lncRNA-疾病关联的拉普拉斯正则化最小二乘模型相结合，以进一步评估其性能。结果，新模型在留一法交叉验证（基于MNDR和Lnc2cancer数据库的AUC分别为0.9316和0.9074）以及五折交叉验证（MNDR和Lnc2cancer数据库的AUC分别为0.9221和0.9033）中获得了可靠的性能，这显著提高了先前模型的预测性能。预计ILNCSIM可作为未来生物医学研究中一种有效的lncRNA功能预测模型。
