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

Mickaël Fabrègue (1) , Sandra Bringay (2, 3) , Pascal Poncelet (2) , Maguelonne Teisseire (4, 2) , Béatrice Orsetti (5)

1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 TATOO - Fouille de données environnementales
3 UPVM - Université Paul-Valéry - Montpellier 3
4 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
5 IRCM - U896 Inserm - UM1 - Institut de recherche en cancérologie de Montpellier

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.

Mots clés: Breast cancer; Translational bioinformatics; Microarrays analysis; Sequential patterns

Domaines: Sciences de l'environnement; Cancer

Mickaël Fabrègue, Sandra Bringay, Pascal Poncelet, Maguelonne Teisseire, Béatrice Orsetti. Mining microarray data to predict the histological grade of a breast cancer. Journal of Biomedical Informatics, 2011, 44 (Suppl 1), pp.S12-S16. ⟨10.1016/j.jbi.2011.03.002⟩. ⟨hal-00654811⟩