Symbolic Data Analysis to Defy Low Signal-to-Noise Ratio in Microarray Data for Breast Cancer Prognosis

Archive ouverte

Hedjazi, Lyamine | Le Lann, Marie-Véronique, V | Kempowsky-Hamon, Tatiana | Dalenc, Florence | Aguilar-Martin, Joseph | Favre, Gilles

Edité par CCSD ; Mary Ann Liebert -

19. International audience. Microarray profiling has brought recently the hope to gain new insights into breast cancer biology and thereby improve the performance of current prognostic tools. However, it also poses several serious challenges to classical data analysis techniques related to the characteristics of resulted data, mainly high-dimensionality and low signal-to-noise ratio. Despite the tremendous research work performed to handle the first challenge in the feature selection framework, very little attention has been directed to address the second one. We propose in this paper to address both issues simultaneously based on symbolic data analysis capabilities in order to derive more accurate genetic marker-based prognostic models. In particular, interval data representation is employed to model various uncertainties in microarray measurements. A recent feature selection algorithm that handles symbolic interval data is used then to derive a genetic signature. The predictive value of the derived signature is then assessed by following a rigorous experimental setup and compared to existing prognostic approaches in terms of predictive performance and estimated survival probability. It is shown that the derived signature (GenSym) performs significantly better than other prognostic models, including the 70-gene signature, St. Gallen and NIH criterions.

• Description
• Sujet/Public
• Outil
• Outil d'anticipation
• Mémoire et thèse
• Gestion