Random Boolean Network Creation, Simulation, and Prediction Toolbox (RaBooNet)

Allows for creation, simulation, and prediction of random Boolean networks.

**Updated on 01/04/2017**
Does not require bioinformatics toolbox.
Based on the paper: http://biorxiv.org/content/early/2016/12/14/094151
The code is split into two parts:
1) NetworkAndSampleCreation
Key functionality:
'makeLeveledHierarchy' - creates a modular, hierarchical
network structure.
'generateSamplesAndFindSSs' - creates random Boolean rules for the base
network, generates a population of samples stemming from this base network, and makes an
assortment of learning problems.

2) LearningFunctions
Key functionality:

'runClassificationsRF' - uses the steady state data generated from 'generateSamplesAndFindSSs' to
classify the samples using random forest. This returns two metrics of classification
performance: classification accuracy and area under the receiver operating characteristic curve.

The above functions are well documented with examples to get you started. Understanding these will allow
you to experiment with a number of different parameters for network/sample creation and learning.

To try out the baseline problem from the paper referenced above, use the script 'longerScript180NodeNetwork.'
Depending on your computer, this could take a little time to run, but probably no more than 10-20
minutes on most modern computers. If time is of the essence, try one of the other three scripts, which in
order from shortest to longest running times will probablybe 'shortScript90NodeNetwork.m', 'shortScript180NodeNetwork',
and 'longerScript90NodeNetwork.'

Before running these, be sure to add the paths in Matlab:

addpath('./NetworkAndSampleCreation')
addpath('./LearningFunctions')