Zhao Xiaowei, Yang Yiqin, Yin Minghao
IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2577-2585. doi: 10.1109/TCBB.2020.2974732. Epub 2021 Dec 8.
In the last few years, accumulating evidences had demonstrated that long non-coding RNAs (lncRNAs) participated in the regulation of target gene expression and played an important role in biological processes and human disease development. Thus, prediction of the associations between lncRNAs and disease had become a hot research in the fields of human sophisticated diseases. Most of these methods considered the information of two networks (lncRNA, disease) while neglected other networks. In this study, we designed a multi-layer network by integrating the similarity networks of lncRNAs, diseases and genes, and the known association networks of lncRNA-disease, lncRNAs-gene, and disease-gene, and then we developed a model called MHRWR for predicting the lncRNA-disease potential associations based on random walk with restart. The performance of MHRWR was evaluated by experimentally verified lncRNA-disease associations based on leave-one-out cross validation. MHRWR obtained a reliable AUC value of 0.91344, which significantly outperformed some previous methods. To further validate the reproducibility of performance, we used the model of MHRWR to verify related lncRNAs of colon cancer, colorectal cancer and lung adenocarcinoma in the case studies. The codes of MHRWR is available on: https://github.com/yangyq505/MHRWR.
在过去几年中，越来越多的证据表明长链非编码RNA（lncRNAs）参与了靶基因表达的调控，并在生物过程和人类疾病发展中发挥着重要作用。因此，预测lncRNAs与疾病之间的关联已成为人类复杂疾病领域的一个热门研究方向。这些方法大多只考虑了两个网络（lncRNA、疾病）的信息，而忽略了其他网络。在本研究中，我们通过整合lncRNAs、疾病和基因的相似性网络以及lncRNA-疾病、lncRNAs-基因和疾病-基因的已知关联网络，设计了一个多层网络，然后我们开发了一种名为MHRWR的模型，用于基于带重启的随机游走预测lncRNA-疾病潜在关联。通过基于留一法交叉验证的实验验证lncRNA-疾病关联来评估MHRWR的性能。MHRWR获得了可靠的AUC值0.91344，显著优于一些先前的方法。为了进一步验证性能的可重复性，我们在案例研究中使用MHRWR模型来验证结肠癌、结直肠癌和肺腺癌的相关lncRNAs。MHRWR的代码可在以下网址获取：https://github.com/yangyq505/MHRWR 。
