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

Authors:
Mickael Fabregue;Sandra Bringay;Pascal Poncelet;Maguelonne Teisseire;BéAtrice Orsetti
Affiliations:
LIRMM UM2 CNRS, UMR 5506 - CC 477, 161 rue Ada, 34095 Montpellier Cedex 5, France;LIRMM UM2 CNRS, UMR 5506 - CC 477, 161 rue Ada, 34095 Montpellier Cedex 5, France and MIAp UM3, Université Paul-Valery, Route de Mende, 34199 Montpellier Cedex, France;LIRMM UM2 CNRS, UMR 5506 - CC 477, 161 rue Ada, 34095 Montpellier Cedex 5, France;CEMAGREF, Maison de la télé-détection, 500 Rue Jean-François Breton, 34000 Montpellier, France;IRCM Institut de Recherche en Cancérologie de Montpellier INSERM U896 - UM1 - CRLC Val d'Aurelle - Paul Lamarque, F-34298 Montpellier Cedex 5, France
Venue:
Journal of Biomedical Informatics
Year:
2011

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Abstract

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.