A network-based method for predicting disease-associated enhancers

Abstract

Background 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. Results 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. Conclusions Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

“…The hub nodes in the DNA methylation layer of the bilayer network aggregated differentially expressed genes. As suggested by Le et al [ 11 ], differentially expressed genes play a crucial role in tumor development.…”

“…Additionally, the robustness of the molecular interaction networks can help us understand the mechanisms behind complex diseases [ 7 , 8 ]. In particular, disease-associated genes need to be identified from the network perspective [ 9 , 10 , 11 ].…”