About TrustRank
TrustRank is a node centrality measure that ranks nodes in a network based on how well they are connected to a (trusted) set of seed nodes (Gyöngyi, Garcia-Molina, and Pedersen 2004).
Find Drugs
Find Drug Targets
Specifies whether also drugs targeting interactors of the seed nodes should be considered.
The larger the damping factor, the faster the trust is propagated through the network.
All nodes with degree greater than this value times number of vertices will be ignored.
Penalty parameter for hubs.
TrustRank is a node centrality measure that ranks nodes in a network based on how well they are connected to a (trusted) set of seed nodes (Gyöngyi, Garcia-Molina, and Pedersen 2004).
Specifies whether also drugs targeting interactors of the seed nodes should be considered.
All nodes with degree greater than this value times number of vertices will be ignored.
Penalty parameter for hubs.
Closeness Centrality is a node centrality measure that ranks the nodes in a network based on the lengths of their shortest paths to all other nodes in the network (Kaczprowski, Doncheva, and Albrecht 2013).
All nodes with degree greater than this value times number of vertices will be ignored.
Degree Centrality assigns an importance score based simply on the number of links held by each node. In CoVex, we use a modified version which does not consider all links but only the neighbouring seeds.
KeyPathwayMiner is a network enrichment tool that identifies condition-specific sub-networks (key pathways) (Alcaraz et al. 2016).
All nodes with degree greater than this value times number of vertices will be ignored.
Penalty parameter for hubs.
The Steiner tree problem is a classical combinatorial optimization problem. It asks to find a sub-graph of minimum size connecting a given set of seed nodes. This implementation behaves non-deterministically, so results can differ between multiple runs.