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Murphy Lab - Software - eLife 2016 - Active Learning Of Perturbations

The software, primary data and intermediate results used for the following paper can be downloaded below:

A. W. Naik, J. D. Kangas, D. P. Sullivan, and R. F. Murphy (2016) Active Machine Learning-driven Experimentation to Determine Compound Effects on Protein Patterns. eLife, in press (DOI).


This source code is released under GNU LGPL v2.1.

Creative Commons License
Images and other materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.


The data and code are contained in the following files.

Source Code

Input Data

Intermediate Results

These files can be used to regenerate the tables and figures in the paper.

Recreating results

To recreate the results from the article (i.e. the figures and tables) from the primary data or intermediate results,

Download and expand the source code to the desired directory.
wget -nc http://murphylab.web.cmu.edu/software/2016_eLife_Active_Learning_Of_Perturbations/eLife_AL_source_code.tgz
tar -xzf eLife_AL_source_code.tgz
Either download and expand the images, additional images and primary data into the same root directory (note that the images tar file is 666 GB),
wget -nc http://murphylab.web.cmu.edu/software/2016_eLife_Active_Learning_Of_Perturbations/eLife_AL_images.tar
tar -xf eLife_AL_images.tar
wget -nc http://murphylab.web.cmu.edu/software/2016_eLife_Active_Learning_Of_Perturbations/eLife_AL_additional_images.tar
tar -xf eLife_AL_additional_images.tar
wget -nc http://murphylab.web.cmu.edu/software/2016_eLife_Active_Learning_Of_Perturbations/eLife_AL_primary_data.tar
tar -xf eLife_AL_primary_data.tar
Or download and expand the intermediate results into the same root directory.
wget -nc http://murphylab.web.cmu.edu/software/2016_eLife_Active_Learning_Of_Perturbations/eLife_AL_intermediate_data.tar
tar -xf eLife_AL_intermediate_data.tar
Go into the code directory and run the code
source RUNME.sh

Note that the script will check whether the intermediate results files are present and skip creating them from the primary data if they are.

System requirements

  • Python 2.7.9 with packages:
    • numpy 1.9.2
    • mahotas 1.3.0
    • scipy 0.15.1
    • sklearn 0.16.0
    • pylab 1.4.3
    • networkx 1.10
This software has been tested using Python 2.7.9 under CentOS 6.5 and Ubuntu 13.10.

Last Updated: 19 Dec 2016

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