Mutual Information NETworks

  • MINET is an open-source Bioconductor package that implements various algorithms for inferring networks (such as gene networks) from data (using mutual information). In particular, the package implements  MRNET, an effective method developed by the authors.

The package was originally written by Patrick E. Meyer, Frederic Lafitte and Gianluca Bontempi (in the Machine Learning Group of the Universite Libre de Bruxelles).

Since 2010 (version 3), MINET, is maintained solely by Patrick E. Meyer, and licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.
Creative Commons License

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In the package:
  • Network inference methods
    • RELNET
    • ARACNE
    • CLR
    • MRNET
    • MRNETB
  • Validation tools
    • PR-Curves
    • ROC
    • Area under curves
    • F-scores

Some papers based on MINET

From the authors
  • Minet: An open source R/Bioconductor package for mutual information based network inference.
    BMC Bioinformatics, 2008.
    P. E. Meyer, F. Lafitte, and G. Bontempi.

  • On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information.
    EURASIP Journal on Bioinformatics and Systems Biology, 2009.
    C. Olsen, P. E. Meyer, and G. Bontempi.

  • Information-Theoretic Inference of Gene Networks Using Backward Elimination.
    In BioComp'10, International Conference on Bioinformatics and Computational Biology, 2010.
    P. E. Meyer, D. Marbach, S. Roy, and M. Kellis.

From other labs
  • Revealing differences in gene network inference algorithms on the network-level by ensemble methods.
    Oxford Bioinformatics, 2010.
    G. Altay and F. Emmert-Streib.
  • Comparative study of grns inference methods based on feature selection by mutual information.
    In IEEE International Workshop on Genomic Signal Processing and Statistics, 2009.

    F. M. Lopes, D. C. Martins, and R. M. Cesar.

  • Recursive regularization for inferring gene networks from time-course gene expression profiles.
    BMC Systems Biology, 3, 2009.

    T. Shimamura, S. Imoto, R. Yamaguchi, A. Fujita, M. Nagasaki, and S. Miyano.