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DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in these experiments can be noisy and have missing values. When it is not ignored, the last issue has been solved by imputing the expression matrix in order to keep going with traditional analysis method. Although it was a first useful step, it is not recommended to use value imputation to deal with missing data. Moreover, appropriate tools are needed to cope with noisy background in expression levels and to take into account a dependency structure among genes under study. Alternative approaches have been proposed but to our knowledge none of them has the ability to fulfil all these features. We therefore propose a clustering algorithm that explicitely accounts for dependencies within a biological network and for missing value mechanism to analyze microarray data. In experiments on synthetic and real biological data, our method demonstrates enhanced results over existing approaches.