• MATLAB® Causal Explorer version 1.5. A Matlab library of computational causal discovery and variable selection algorithms. Initial version of the library is described in
    CF Aliferis, I Tsamardinos, A Statnikov. “Causal Explorer: A Probabilistic Network Learning Toolkit for Biomedical Discovery.” The 2003 International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS ’03), June 23-26, 2003.
    Link to the library

  • SCENERY: a web-based application for network reconstruction, statistical analysis and visualization of cytometry (single-cell) data.
    SCENERY link

  • Code for the publication “A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions”:
    Link to the code

  • Code for the publication “omicsNPC: applying the Non-Parametric Combination methodology to the integrative analysis of heterogeneous omics data”:
    Link to the code

  • MATLAB® implementation of the algorithms described in the paper Scoring and Searching over Bayesian Networks with Informative, Causal and Associative Priors.
    [ Download .zip file ]

  • ΄MXM΄ package (v. 1.4.2) is an R package that implements feature selection methods for identifying minimal, statistically-equivalent and equally predictive feature subsets. Bayesian network algorithms and related functions are also included, while a variety of statistical conditional independence tests and cross validation strategies are available. The package name ‘MXM’ stands for ‘Mens eX Machina’, meaning ‘Mind from the Machine’ in Latin.

  • MATLAB® code used for the experiments reported in the paper Tools and Algorithms for Causally Interpreting Directed Edges in Maximal Ancestral Graphs.
    [ Download .zip file ]

  • MATLAB® code used for the experiments reported in the paper Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs.
    [ Download .zip file ]

  • MATLAB® code used for the experiments reported in the paper Towards Integrative Causal Analysis of Heterogeneous Datasets and Studies.
    [ Download .zip file ]

  • MATLAB® code used for the experiments reported in the paper Learning Causal Structure From Overlapping Variable Sets (presented at AISTATS 2010).
    [ Download .zip file ]

  • GEMS (Gene Expression Model Selector) is a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data developed at Vanderbilt University in collaboration with Dr. Ioannis Tsamardinos.
    [ Visit GEMS website ]

  • MATLAB® and R code used for the experiments reported in the paper Structure – based variables selection for Survival Data (submitted to Bioinformatics).
    [ Download .zip file ]

  • Mens X Machina Commons Toolbox is a MATLAB® toolbox with common functions used in other toolboxes provided by the Mens X Machina group.

  • Mens X Machina Probabilistic Graphical Model Toolbox is a MATLAB toolbox that aims to provide a comprehensive set of tools for Bayesian Networks and other probabilistic graphical models. Currently only local BN learning and BN skeleton learning using the MMPC algorithm is implemented. Support for full structure learning and inference will be added in future versions.