Drug-disease proximity based therapeutic effect screening and analysis
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.



Jupyter (IPython) Notebook and required files for the proximity-based analysis in the "Network-based in silico drug efficacy screening" manuscript. Known drug-disease associations, proximity and relative efficacy values are given in "proximity.dat" and "palliative.csv" files.

Required packages

  • R kernel for Jupyter
  • ROCR (or pROC)
  • ggplot2
  • beanplot

Calculating proximity

See toolbox package for calculating proximity.

For instance, to calculate the proximity from (A, C) to (B, D, E) in a toy network:

>>> from toolbox import wrappers
>>> file_name = "data/toy.sif"
>>> network = wrappers.get_network(file_name, only_lcc = True)
>>> nodes_from = ["A", "C"]
>>> nodes_to = ["B", "D", "E"]
>>> d, z, (mean, sd) = wrappers.calculate_proximity(network, nodes_from, nodes_to, min_bin_size = 2, seed=452456)
>>> print (d, z, (mean, sd))
(1.0, 1.3870748387117167, (0.67100000000000004, 0.2371897974197035))

Data files

The data folder contains

  • disease/disease_genes.tsv: (MeSH term, gene ids) Disease-gene associations for MeSH disease terms curated in Menche et al. (2015, Science)
  • target/drug_to_geneids.pcl.all: (DrugBank id, gene ids) Drug targets for all the drugs in Drugbank
  • indication/disease_to_drugs.pcl.source: (MeSH term, DrugBank ids) Drug indication information from "source" database (MEDI, Metab2MeSH, KEGG or NDFRT)
  • network/network.sif: (Geneid 1 Geneid) Human interactome curated in Menche et al.
  • toy.sif: toy network used in the example

Note that the analysis in the paper (whose citation below), uses a subset of the drugs and diseases given in these files, see Methods in the paper for details.


Guney E, Menche J, Vidal M, Barabási AL. Network-based in silico drug efficacy screening. Nat. Commun. 7:10331 doi: 10.1038/ncomms10331 (2016). link