The management of the Institute of Computer Science, Foundation of Research and Technology, Hellas has recently (Oct. 2010) decided to found the Bioinformatics Laboratory, with the Mens X Machina group members as its founding and core members and Prof. Tsamardinos as its first Head. The decision reflects the commitment of the management to progressing and advancing Bioinformatics in the Institute, to provide our group with administrative autonomy, and recognizes the contributions of our group to the field. The Mens X Machina group will continue its mission as Bioinformatics Laboratory. The Mens X Machina website will still be maintained but a new updated Bioinformatics Laboratory website will be available within a couple of months.
New versions (0.9.2) of our MATLAB® toolboxes have been released.
New features in version 0.9.2 of Mens X Machina Probabilistic Graphical Model Toolbox (MxM PGM) include:
- Skeleton identification error estimation/calculation utilities
- Templates for custom functions, handles to which can be used as values of skeleton identification function parameters.
New features in version 0.9.2 of Mens X Machina Commons Toolbox include multiple testing error estimation/calculation utilities.
Also, a supplement to MxM PGM called Mens X Machina Probabilistic Graphical Model Samples (MxM PGM Samples) is available to download. MxM PGM Samples is a collection of samples from several Bayesian networks.
New versions (0.9.1) of our MATLAB® toolboxes have been released. The Mens X Machina Bayesian Network Toolbox has been renamed to Probabilistic Graphical Model Toolbox, because we intent to include functionality that covers other types of graphical models such as Maximal Ancestral Graphs (MAGs) and Pairwise Casual Graphs (PCGs) in the future.
New features in version 0.9.1 of Mens X Machina Probabilistic Graphical Model Toolbox include:
- Creating and sampling Gaussian Bayesian Networks
- More customizable MMPC via new parameters
- MMPC support for continuous data by employing the Fisher’s Z test of conditional independence
The most prominent addition in version 0.9.1 of Mens X Machina Commons Toolbox is the Fisher’s Z test of conditional independence.
Unfortunately, because the previous versions were the first ones and many things had not yet been settled, the new versions are not backward-compatible in general, but the Compatibility Considerations sections in the accompanying release notes should be helpful in migration. Hopefully, from now on the packages will be backward-compatible.
A new Bioinformatics paper adapts and compares MMPC for molecular signature discovery in high-dimensional, micro-array gene-expression survival data outperforming the state-of-the-art in the field. The source code of the algorithm and experimental scripts are available in the Software section.
We have just released the first versions of our toolboxes for MATLAB®:
Mens X Machina Bayesian Network Toolbox aims to provide a comprehensive set of tools for bayesian network inference and structure learning. Currently only bayesian network skeleton learning using the MMPC algorithm is implemented. Support for full structure learning and inference will be added in future versions.
Mens X Machina Commons Toolbox is a MATLAB toolbox with common functions (to be) used in other toolboxes provided by Mens X Machina, including the Bayesian Network Toolbox.
