Mining microarray data to predict the histological grade of a breast cancer

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Fabrègue, Mickaël | Bringay, Sandra | Poncelet, Pascal | Teisseire, Maguelonne | Orsetti, Béatrice

Edité par CCSD ; Elsevier -

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE. BACKGROUND: The aim of this study was to develop an original method to extract sets of relevant molecular biomarkers (gene sequences) that can be used for class prediction and can be included as prognostic and predictive tools. MATERIALS AND METHODS: The method is based on sequential patterns used as features for class prediction. We applied it to classify breast cancer tumors according to their histological grade. RESULTS: We obtained very good recall and precision for grades 1 and 3 tumors, but, like other authors, our results were less satisfactory for grade 2 tumors. CONCLUSIONS: We demonstrated the interest of sequential patterns for class prediction of microarrays and we now have the material to use them for prognostic and predictive applications.

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