Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks.

More and more evidence showed that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human sophisticated diseases. Therefore, predicting human lncRNA-disease associations is a challenging and urgently task in bioinformatics to research of human sophisticated diseases. In the work, a global network-based computational framework called as LRWRHLDA were proposed which is a universal network-based method. Firstly, four isomorphic networks include lncRNA similarity network, disease similarity network, gene similarity network and miRNA similarity network were constructed. And then, six heterogeneous networks include known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations network were applied to design a multi-layer network. Finally, the Laplace normalized random walk with restart algorithm in this global network is suggested to predict the relationship between lncRNAs and diseases. The ten-fold cross validation is used to evaluate the performance of LRWRHLDA. As a result, LRWRHLDA achieves an AUC of 0.98402, which is higher than other compared methods. Furthermore, LRWRHLDA can predict isolated disease-related lnRNA (isolated lnRNA related disease). The results for colorectal cancer, lung adenocarcinoma, stomach cancer and breast cancer have been verified by other researches. The case studies indicated that our method is effective. The online version contains supplementary material available at 10.1186/s12859-021-04538-1.

越来越多的证据表明，长链非编码RNA（lncRNAs）在人类复杂疾病的发生和发展中起着重要作用。因此，预测人类lncRNA - 疾病关联是生物信息学中研究人类复杂疾病的一项具有挑战性且紧迫的任务。 在这项工作中，提出了一种基于全局网络的计算框架，称为LRWRHLDA，它是一种通用的基于网络的方法。首先，构建了四个同构网络，包括lncRNA相似性网络、疾病相似性网络、基因相似性网络和miRNA相似性网络。然后，应用六个异构网络，包括已知的lncRNA - 疾病、lncRNA - 基因、lncRNA - miRNA、疾病 - 基因、疾病 - miRNA和基因 - miRNA关联网络来设计一个多层网络。最后，建议在这个全局网络中使用拉普拉斯归一化随机游走重启动算法来预测lncRNAs与疾病之间的关系。 采用十折交叉验证来评估LRWRHLDA的性能。结果，LRWRHLDA的AUC达到0.98402，高于其他对比方法。此外，LRWRHLDA能够预测孤立的疾病相关lnRNA（孤立的lnRNA相关疾病）。对结直肠癌、肺腺癌、胃癌和乳腺癌的研究结果已被其他研究证实。案例研究表明我们的方法是有效的。 在线版本包含补充材料，可在10.1186/s12859 - 021 - 04538 - 1获取。