Li Yahan, Zhang Mingrui, Shang Junliang, Li Feng, Ren Qianqian, Liu Jin-Xing
School of Computer Science, Qufu Normal University, Rizhao, China.
Front Genet. 2023 Aug 8;14:1249171. doi: 10.3389/fgene.2023.1249171. eCollection 2023.
Identification of disease-associated long non-coding RNAs (lncRNAs) is crucial for unveiling the underlying genetic mechanisms of complex diseases. Multiple types of similarity networks of lncRNAs (or diseases) can complementary and comprehensively characterize their similarities. Hence, in this study, we presented a computational model iLncDA-RSN based on reliable similarity networks for identifying potential lncRNA-disease associations (LDAs). Specifically, for constructing reliable similarity networks of lncRNAs and diseases, miRNA heuristic information with lncRNAs and diseases is firstly introduced to construct their respective Jaccard similarity networks; then Gaussian interaction profile (GIP) kernel similarity networks and Jaccard similarity networks of lncRNAs and diseases are provided based on the lncRNA-disease association network; a random walk with restart strategy is finally applied on Jaccard similarity networks, GIP kernel similarity networks, as well as lncRNA functional similarity network and disease semantic similarity network to construct reliable similarity networks. Depending on the lncRNA-disease association network and the reliable similarity networks, feature vectors of lncRNA-disease pairs are integrated from lncRNA and disease perspectives respectively, and then dimensionality reduced by the elastic net. Two random forests are at last used together on different lncRNA-disease association feature sets to identify potential LDAs. The iLncDA-RSN is evaluated by five-fold cross-validation to analyse its prediction performance, results of which show that the iLncDA-RSN outperforms the compared models. Furthermore, case studies of different complex diseases demonstrate the effectiveness of the iLncDA-RSN in identifying potential LDAs.
识别与疾病相关的长链非编码RNA（lncRNA）对于揭示复杂疾病的潜在遗传机制至关重要。多种类型的lncRNA（或疾病）相似性网络可以相互补充并全面表征它们的相似性。因此，在本研究中，我们提出了一种基于可靠相似性网络的计算模型iLncDA-RSN，用于识别潜在的lncRNA-疾病关联（LDA）。具体而言，为了构建可靠的lncRNA和疾病相似性网络，首先引入与lncRNA和疾病相关的miRNA启发式信息来构建它们各自的杰卡德相似性网络；然后基于lncRNA-疾病关联网络提供lncRNA和疾病的高斯相互作用轮廓（GIP）核相似性网络和杰卡德相似性网络；最后在杰卡德相似性网络、GIP核相似性网络以及lncRNA功能相似性网络和疾病语义相似性网络上应用带重启策略的随机游走，以构建可靠的相似性网络。根据lncRNA-疾病关联网络和可靠的相似性网络，分别从lncRNA和疾病角度整合lncRNA-疾病对的特征向量，然后通过弹性网络进行降维。最后，在不同的lncRNA-疾病关联特征集上一起使用两个随机森林来识别潜在的LDA。通过五折交叉验证对iLncDA-RSN进行评估以分析其预测性能，结果表明iLncDA-RSN优于比较模型。此外，对不同复杂疾病的案例研究证明了iLncDA-RSN在识别潜在LDA方面的有效性。
