School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.
PLoS One. 2021 Dec 8;16(12):e0260432. doi: 10.1371/journal.pone.0260432. eCollection 2021.
Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers.
In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases.
Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.
增强子调节靶基因的转录，导致表达水平的变化。因此，增强子的异常活性可导致疾病。迄今为止，已经鉴定出大量的增强子，但只有一小部分被发现与疾病有关。这就迫切需要开发计算方法来预测疾病与增强子之间的关联。
在这项研究中，我们假设共享靶基因的增强子可能与类似的疾病有关，从而可以预测它们之间的关联。因此，我们通过连接显著共享靶基因的增强子构建了一个增强子功能相互作用网络，然后基于随机游走重启动算法开发了一种网络扩散方法 RWDisEnh，用于在疾病和增强子网络上全局测量疾病和增强子之间的关联程度。当将疾病相似性与增强子功能相互作用网络集成到已知的疾病-增强子关联中，形成疾病和增强子的异质网络时，RWDisEnh 的性能最佳。它也优于另一种网络扩散方法，即带有先验的 PageRank，以及一种基于邻居的方法，即 MaxLink，它只是选择已知与疾病相关的增强子的最近邻居。最后，我们表明 RWDisEnh 可以预测与疾病直接或间接相关的新的增强子。
总的来说，RWDisEn 可能是一种预测疾病-增强子关联的潜在方法。
