Network Proximity Tools

Configuration File: network_proximity_tools.json Tool Type: Local Tools Count: 1

This page contains all tools defined in the network_proximity_tools.json configuration file.

Available Tools

Network_proximity (Type: NetworkProximityTool)

Graph distance between two node sets on a user-supplied network, with the standard measures from …

Network_proximity tool specification

Tool Information:

  • Name: Network_proximity

  • Type: NetworkProximityTool

  • Description: Graph distance between two node sets on a user-supplied network, with the standard measures from Guney/Barabasi (2016) and Menche (2015) plus a degree-matched random Z-score. Domain-agnostic: any two node sets on any graph (drug targets vs disease module, two pathways, two marker sets, …). Give the network as inline ‘edges’ pairs or a 2-column ‘edgelist_path’, plus two node sets (‘set_a’/’set_b’, or the drug-pharmacology aliases ‘targets’/’disease_genes’). measure=’closest’ (default), ‘shortest’, or ‘separation’ (s_AB<0 = overlapping modules). Returns the value, Z-score, and empirical p. Pure networkx+numpy, no network call or API key.

Parameters:

  • edges ([‘array’, ‘null’]) (optional) Network as inline [source, target] node-id pairs (undirected). Use for small/medium graphs.

  • edgelist_path ([‘string’, ‘null’]) (optional) Alternative to inline: path to a 2-column edgelist (.tsv/.txt = tab, else comma)

  • set_a ([‘array’, ‘null’]) (optional) First node set

  • set_b ([‘array’, ‘null’]) (optional) Second node set

  • targets ([‘array’, ‘null’]) (optional) Alias for set_a (drug targets)

  • disease_genes ([‘array’, ‘null’]) (optional) Alias for set_b (disease-module genes)

  • measure ([‘string’, ‘null’]) (optional) Distance measure: ‘closest’ (default, Guney), ‘shortest’ (all-pairs mean), or ‘separation’ (Menche s_AB)

  • n_rand ([‘integer’, ‘null’]) (optional) Degree-matched randomizations for the null (default 1000)

  • seed ([‘integer’, ‘null’]) (optional) Random seed for reproducibility (default 42)

Example Usage:

query = {
    "name": "Network_proximity",
    "arguments": {
    }
}
result = tu.run(query)