JSOTA
Brief info

This is BIOALMA´s propietary clustering algorithm for microarray analysis. The Java Self-Organising Tree Algorithm (JSOTA), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. JSOTA clustering confers several advantages over classical clustering methods (see article).

 


More

JSOTA is an implementation of the JSOTA Algorithm that includes a window interface written in Java and the JSOTA core written in C++. This makes the program very easy to use whilst ensuring that the computationally-intensive part runs very fast. It is available for a wide range of platforms.

  • JSOTA is a divisive method. The growing can be stopped at the desired hierarchical level.
  • A criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided.
  • Obtaining average gene expression patterns is a built-in feature of the algorithm.
  • Since JSOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data, making it possible to use the system on small computers.
  • JSOTA can be applied to any data providing that they can be coded as a series of numbers and that a computable measure of similarity between data items can be used.
  • JSOTA maps Gene Ontology data into the structure of clusters.

You can test the power of the JSOTA algorithm by clicking on the next button:

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