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.
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))
>>>
The data folder contains
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