Quick Start

ModMashup.jl

Required Dependencies

julia v0.5 +

You can download latest Julia from the official website. Version 0.5 or higher is highly recommended.

Installation

Enter Julia REPL.

$ julia

Then run the command below in Julia REPL.

Pkg.rm("ModMashup")
Pkg.clone("https://github.com/memoiry/ModMashup.jl")

Example usage in Julia

Usage 1: Mashup Feature Selection

import ModMashup
cd(joinpath(Pkg.dir("ModMashup"), "test/data"))

#Set up database information
dir = "networks"
labels = "target.txt"
querys = "."
id = "ids.txt"
smooth = true
top_net = "nothing"

# Construct the dabase, which contains the preliminary file.
database = ModMashup.Database(dir, id,
                           querys, labels_file = labels,
                           smooth = smooth,
                           int_type = :selection,
                           top_net = top_net)

# Define the algorithm you want to use to integrate the networks
model = ModMashup.MashupIntegration()

# Running network integration
ModMashup.network_integration!(model, database)

net_weights = ModMashup.get_weights(model)
tally = ModMashup.get_tally(model)

Usage 2: Mashup query runner for patients ranking using selected networks

import ModMashup
cd(joinpath(Pkg.dir("ModMashup"), "test/data"))

#Set up database information
dir = "networks"
querys = "CV_1.query"
id = "ids.txt"
smooth = true
# Top_networks contains selected top ranked networks.
top_net = "top_networks.txt"

# Construct the dabase, which contains the preliminary file.
database = ModMashup.Database(dir, id, 
   querys, smooth = smooth,
   int_type = :ranking,
   top_net = top_net)

# Define the algorithm you want to use to integrate the networks
int_model = ModMashup.MashupIntegration()
lp_model = ModMashup.LabelPropagation(verbose = true)

# Running network integration
ModMashup.fit!(int_model, lp_model, database)

# Pick up the result
#combined_network = ModMashup.get_combined_network(int_model)
net_weights = ModMashup.get_weights(int_model)
score = ModMashup.get_score(lp_model)