![]() Network analysis can help us understand the function of an individual node and the collaboration between other nodes. These data can be represented as networks, in which the nodes as surrogates for proteins, metabolites, or transcripts, are connected by edges to show the interactions, reactions, or regulations among nodes. Recent breakthroughs in high-throughput techniques lead experimental data deluges in genomics, proteomics, transcriptomics, metabolomics and interactomics. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. It computes all eleven methods in one stop shopping way. ConclusionsĬytoHubba provide a user-friendly interface to explore important nodes in biological networks. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. We introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks. Network is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions.
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