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MALA - MicroArray Logic Analyzer

MALA is specifically designed for the analysis of Microarray data. The rational data representing the gene expressions is discretized into a limited number of intervals for each cell of the array; the obtained discrete variables are then used to select a small subset of the genes that have strong discriminating power for the considered classes. The optimization algorithms for feature selection and logic formula extraction are then used to identify networks of genes - and related thresholds on their expression level - that characterize the classes. For the input file, follow the description below. The parameters needed for the correct running of MALA is a file in DMBCSV format.
You can download an input example file here.

You can find diverse offline versions of MALA here.

Command Line Versions


Offline Graphic User Interface Version

Sample datasets


MALA adopts a logic data mining method, which is composed of three main steps:

  1. the optional application of discrete cluster analysis (DCA) an efficient gene expression clustering method
  2. the selection of the most relevant (clusters of) genes (feature selection)
  3. the identification of the logic formulas that best characterize the microarray samples (formula extraction)
The MicroArray Logic Analyzer (MALA) software system implements the methods described above: clustering, feature selection and logic formulas extraction.

Below you can see the component diagram of MALA.