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Single miRNAs can regulate hundreds of mRNAs, enabling miRNAs to orchestrate robust biological responses by simultaneously impacting multiple gene networks.
Motivated by this understanding, we proposed a novel method called REGENT for integrating multiple gene networks with GWAS data to prioritize candidate genes for complex diseases.
We leveraged a technique called the network representation learning to embed a gene network into a compact and robust feature space, and then designed a hierarchical statistical model to integrate features of multiple gene networks with GWAS data for the effective inference of genes associated with a disease of interest.
Therefore, DNA methylation in cancer would be predicted to influence multiple gene networks rather than single genes.
The miRNA-based cancer gene therapy offers the theoretical appeal of targeting multiple gene networks that are controlled by a single, aberrantly expressed miRNA (Tong and Nemunaitis, 2008).
To contribute to fill this gap, in this paper we propose, compare and analyze different network integration strategies to combine multiple gene networks constructed with different sources of single or heterogeneous data.
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One should note that star-shaped 'one miRNA regulates multiple genes' networks are the most basic structure in the bipartite network that can be extracted directly.
In this study, we develop a node-based multi-view differential network analysis model to simultaneously estimate multiple gene regulatory networks and their differences from multi-platform gene expression data.
This is the case of popular approaches such as WGCNA [ 65] -targeted to create scale-free networks from gene co-expression analysis- and GeneMANIA [ 66], focused in the integration of multiple gene association networks.
If one assumes that domestication involves a modest number of mutations, it seems reasonable to conclude that these mutations have had far reaching, though often relatively small, effects on multiple gene expression networks.
Moreover, the fact that C/EBPδ specifically interacts with over 1000 genes, independently of C/EBPβ, suggests that C/EBPδ may play a unique and specific role in the control of cell behaviour through the independent regulation of multiple gene regulatory networks.
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multiple gene models
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