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Microarray data appear particularly useful to investigate mechanisms in cancer biology and represent one of the most powerful tools to uncover the genetic mechanisms causing loss of cell cycle control. Recently, several different methods to employ microarray data as a diagnostic tool in cancer classification have been proposed. These procedures take changes in the expression of particular genes into account but do not consider disruptions in certain gene interactions caused by the tumor. It is probable that some genes participating in tumor development do not change their expression level dramatically. Thus, they cannot be detected by simple classification approaches used previously. For these reasons a classification procedure exploiting information related to changes in gene interactions is needed.

We developed a MAximal MArgin Linear Programming (MAMA) method (Antonov et al., 2004) for the classification of tumor samples based on microarray data. MAMA system was developed using the linear programming system LINDO. This procedure detects groups of genes and constructs models (features) that strongly correlate with particular tumor types. The detected features include genes whose functional relations are changed for particular cancer types. The proposed method was tested on two publicly available datasets and demonstrated a prediction ability superior to previously employed classification schemes (Antonov et al., 2004). The MDCS server provides an easy access to MAMA classification tool. We hope that you will find it convenient and useful for your work.

Acknowledgement We thank LINDO Inc. for their kind permission to use LINDO in this web-interface and INTAS grant, Virtual Computational Chemistry Laboratory for the partial financial support.


Antonov, A.V., Tetko, I.V, Prokopenko, V.V., Kosykh,D. & Mewes, H.W. Web Portal for Classification of Expression Data using Maximal Margin Linear Programming, Bioinformatics, 2004, 20, 3284-5.

Antonov, A. V., Tetko, I. V., Mader, M. T., Budczies, J. & Mewes, H. W. Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics, 2004, 20, 644-52.

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