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--- software [2011/03/21 10:02]
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 [[http://www.cellillustrator.com/|{{ciclassic.png|}}]] Cell Illustrator allows users to intuitively model, visualize, and simulate various biological pathways using Cell System Markup Language (CSML). For the detailed introductions and features, please refer to the website of [[http://www.cellillustrator.com/|Cell Illustrator]]. You can download the latest version of Cell Illustrator Pro and Cell Illustration Draw from above website by clicking on the item "Trial License - CI Pro" or "Free License - CI Draw" from the menu bar on the left side. To use CI Pro or CI Draw, you should finish a light registration procedure. After the initial registration and installation, you have the following trial period respectively: [[http://www.cellillustrator.com/register|CI Pro workes as a 1-month trial version]]. [[http://www.cellillustrator.com/registerdraw|CI Draw is now free for the users and you have 1-year to trial the product]]. You can also access to [[http://www.cellillustrator.com/dds|Cell Illustrator Download/Documentation/Support page]] for download.
-[[http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/index.html|{{cluster.gif}}]] We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code.
+[[http://bonsai.hgc.jp/~mdehoon/software/cluster/index.htm|{{cluster.gif}}]] We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code.
 [[http://www.ism.ac.jp/~higuchi/arraycluster.htm|{{fetch.png|}}]] One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail due to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.