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        "author": "Thomas Moerman",
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        "description": ".. image:: img/arboretum.png\n    :alt: arboretum\n    :scale: 100%\n    :align: left\n\n.. image:: https://travis-ci.org/tmoerman/arboretum.svg?branch=master\n    :alt: Build Status\n    :target: https://travis-ci.org/tmoerman/arboretum\n\n.. image:: https://readthedocs.org/projects/arboretum/badge/?version=latest\n    :alt: Documentation Status\n    :target: http://arboretum.readthedocs.io/en/latest/?badge=latest\n\n.. image:: https://img.shields.io/badge/pypi-0.1.3-blue.svg\n    :alt: PyPI package\n    :target: https://pypi.python.org/pypi?:action=display&name=arboretum&version=0.1.3\n\n----\n\n.. epigraph::\n\n    *The most satisfactory definition of man from the scientific point of view is probably Man the Tool-maker.*\n\n.. _arboretum: https://arboretum.readthedocs.io\n.. _`arboretum documentation`: https://arboretum.readthedocs.io\n.. _notebooks: https://github.com/tmoerman/arboretum/tree/master/notebooks\n.. _issue: https://github.com/tmoerman/arboretum/issues/new\n\n.. _dask: https://dask.pydata.org/en/latest/\n.. _`dask distributed`: https://distributed.readthedocs.io/en/latest/\n\n.. _GENIE3: http://www.montefiore.ulg.ac.be/~huynh-thu/GENIE3.html\n.. _`Random Forest`: https://en.wikipedia.org/wiki/Random_forest\n.. _ExtraTrees: https://en.wikipedia.org/wiki/Random_forest#ExtraTrees\n.. _`Stochastic Gradient Boosting Machine`: https://en.wikipedia.org/wiki/Gradient_boosting#Stochastic_gradient_boosting\n.. _`early-stopping`: https://en.wikipedia.org/wiki/Early_stopping\n\nInferring a gene regulatory network (GRN) from gene expression data is a computationally expensive task, exacerbated by increasing data sizes due to advances\nin high-throughput gene profiling technology.\n\nThe arboretum_ software library addresses this issue by providing a computational strategy that allows executing the class of GRN inference algorithms\nexemplified by GENIE3_ [1] on hardware ranging from a single computer to a multi-node compute cluster. This class of GRN inference algorithms is defined by\na series of steps, one for each target gene in the network, where the most important candidates from a set of regulators are determined from a regression\nmodel to predict a target gene's expression profile.\n\nMembers of the above class of GRN inference algorithms are attractive from a computational point of view because they are parallelizable by nature. In arboretum,\nwe specify the parallelizable computation as a dask_ graph [2], a data structure that represents the task schedule of a computation. A dask scheduler assigns the\ntasks in a dask graph to the available computational resources. Arboretum uses the `dask distributed`_ scheduler to\nspread out the computational tasks over multiple processes running on one or multiple machines.\n\nArboretum currently supports 2 GRN inference algorithms:\n\n1. **GRNBoost2**: a novel and fast GRN inference algorithm using `Stochastic Gradient Boosting Machine`_ (SGBM) [3] regression with `early-stopping`_ regularization.\n2. **GENIE3**: the classic GRN inference algorithm using `Random Forest`_ (RF) or ExtraTrees_ (ET) regression.\n\nReferences\n**********\n\n1. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE\n2. Rocklin, M. (2015). Dask: parallel computation with blocked algorithms and task scheduling. In Proceedings of the 14th Python in Science Conference (pp. 130-136).\n3. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367-378.\n4. Marbach, D., Costello, J. C., Kuffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., ... & Dream5 Consortium. (2012). Wisdom of crowds for robust gene network inference. Nature methods, 9(8), 796-804.\n\nGet Started\n***********\n\nArboretum was conceived with the working bioinformatician or data scientist in mind. We provide extensive documentation and examples to help you get up to speed with the library.\n\n* Read the `arboretum documentation`_.\n* Browse example notebooks_.\n* Report an issue_.\n\nLicense\n*******\n\nBSD 3-Clause License\n\n\n",
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