![]() It is basically defined as the number of shortest paths in the graph that pass through the node divided by the total number of shortest paths.īetweenness centrality measures how often a node occurs on all shortest paths between two nodes. The calculation of betweenness centrality is not standardised and there are many ways solve it. By combining this data with interference analysis we can simulate targeted attacks on protein-protein interaction networks and predict which proteins are better drug candidates, for example see Yu, et al 2007 ( 15). These nodes can represent important proteins in signalling pathways and can form targets for drug discovery. Assessing reliability and measuring confidenceĪll materials are free cultural works licensed under a Creative CommonsĪttribution 4.0 International (CC BY 4.0) license, except where further licensing details are provided.īetweenness centrality is based on communication flow. Nodes with a high betweenness centrality are interesting because they lie on communication paths and can control information flow.Network representation and analysis tools.Properties of PPINs: scale-free networks. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |