minepy - Maximal Information-based Nonparametric Exploration
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minepy provides a library for the Maximal Information-based
Nonparametric Exploration (MIC and MINE family). Key features:
- APPROX-MIC (the original algorithm, DOI: 10.1126/science.1205438) and
MIC_e (DOI: arXiv:1505.02213 and DOI: arXiv:1505.02214) estimators;
- Total Information Coefficient (TIC, DOI: arXiv:1505.02213) and the
Generalized Mean Information Coefficient (GMIC, DOI: arXiv:1308.5712);
- an ANSI C library
- a C++ interface;
- an efficient Python API (Python 2 and 3 compatibility);
- an efficient MATLAB/OCTAVE API;
minepy is an open-source, GPLv3-licensed software.
The minerva R interface is available at CRAN <https://cran.r-project.org/web/packages/minerva/index.html>_.
MICtools
The mine command-line application is deprecated since version 1.2.2.
We suggest to use MICtools, a comprehensive and effective pipeline for TICe and MICe
analysis. TICe is used to perform efficiently a high throughput
screening of all the possible pairwise relationships assessing their
significance, while MICe is used to rank the subset of significant associations
on the bases of their strength. Paper <https://academic.oup.com/gigascience/article/7/4/giy032/4958979>,
code and documentation <https://github.com/minepy/mictools>.
Docker image
^^^^^^^^^^^^
The minepy library is preinstalled in the MICtools Docker image <https://hub.docker.com/r/minepy/mictools/>_.
Links
Homepage and Documentation <http://minepy.readthedocs.io>_Download <https://github.com/minepy/minepy/releases>_Github page <https://github.com/minepy/minepy>_Issues <https://github.com/minepy/minepy/issues>_Old (version 1.0.0) documentation <http://minepy.sourceforge.net/docs/1.0.0/>_
Citing minepy
Davide Albanese, Michele Filosi, Roberto Visintainer, Samantha Riccadonna,
Giuseppe Jurman and Cesare Furlanello. minerva and minepy: a C engine for the
MINE suite and its R, Python and MATLAB wrappers. Bioinformatics (2013)
29(3): 407-408 first published online December 14, 2012
doi:10.1093/bioinformatics/bts707.
Financial Contributions
^^^^^^^^^^^^^^^^^^^^^^^
Computational Biology Unit - Research and Innnovation Center at Fondazione Edmund Mach <http://www.fmach.it/eng>_Predictive Models for Biological and Environmental Data Analysis (MPBA) Research Unit at Fondazione Bruno Kessler <http://mpba.fbk.eu>_