Our efforts in this study will be useful to further reveal the soybean mi RNAmi RNA and mi RNAgene interactive mechanism on a systematic level.
Based on these corpora, we construct 6 weighted and directed word co-occurrence networks.The approach performs well when evaluated against similar tools and smaller overall module size allows for more specific functional annotation and facilitates the interpretation of these modules.Identification of protein complexes is critical to understand complex formation and protein functions.A few studies have focused on the well-studied human species; however, these methods can neither be extended to other non-model organisms nor take fully into account the information embedded in mi RNAtarget and targettarget interactions.Thus, it is important to develop appropriate methods for inferring the mi RNA network of non-model species, such as soybean (Glycine max), without such extensive mi RNA-phenotype associated data as mi RNA-disease associations in human.
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Compared with other highly cited module-finding tools, j Active Modules and Matisse, Walktrap-GM shows strong performance in the discovery of modules enriched with known cancer genes.Conclusions: These results demonstrate that the Walktrap-GM algorithm identifies modules significantly enriched with cancer genes, their joint effects and promising candidate genes.We apply the proposed method (called CPredictor) to two PPI data sets of S. Experimental results show that CPredictor outperforms the existing methods. The outstanding precision of CPredictor proves that the from-function-to-interaction paradigm provides a new and effective way to computational detection of protein complexes.We found that mi RFNs of soybean exhibit a scale-free, small world and modular architecture, with their degrees fit best to power-law and exponential distribution.
We also showed that mi RNA with high degree tends to interact with those of low degree, which reveals the disassortativity and modularity of mi RFNs.
Then, we map the resulting protein clusters onto a PPI network (PIN in short), extract connected subgraphs consisting of clustered proteins from the PPI network and expand each connected subgraph with protein nodes that have rich links to the proteins in the subgraph.
Such expanded subgraphs are taken as predicted complexes.
Network communicability considers all paths of all lengths between two network members.
Given the success of previous network analyses of proteinprotein interactions, we applied the concepts of network communicability to this problem.