Abstract
MicroRNA (miRNA)-mRNA regulatory modules are key entities to disorders. Several computational methods are developed to identify miRNA-mRNA modules. Although these methods have achieved ideal performance, the number of modules needed to be predefined. Therefore, identification of modules is still computationally challenging. In this study, a new algorithm called MiRMD (miRNA-mRNA Regulatory Modules Detection) is presented to identify miRNA-mRNA modules, which do not need to predefine the number of modules. Firstly, a miRNA-mRNA regulatory network is constructed, then core structures are detected in this network by merging cohesive modules. Next, some overlapping neighbor nodes are added into the cores according to the density. Finally, some overlap modules are filtered. The experimental results based on three cancers datasets show that modules identified by MiRMD are more coherent and functional enriched than the other two methods according to MiMEC and GO enrichment. Particularly, modules identified by our method are strongly implicated in cancer.
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Acknowledgments
The authors would like to acknowledge the assistance provided by National Natural Science Foundation of China (Grant nos. 61572180 and 61472467) and Hunan Provincial Natural Science Foundation of China (Grant no. 13JJ2017).
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Luo, J., Liu, B., Cao, B., Wang, S. (2016). Identifying miRNA-mRNA Regulatory Modules Based on Overlapping Neighborhood Expansion from Multiple Types of Genomic Data. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_23
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