Computational intelligence in clinical oncology: Lessons learned from an analysis of a clinical study

Abstract

In this chapter, we present a retrospective clinical study where the adoption of computational intelligence approaches for performing knowledge extraction from gene expression data enabled an improved oncological clinical analysis. This study focuses on a survival analysis of estrogen receptor (ER) positive breast cancer patients treated with tamoxifen. The chapter describes each step of the gene expression data analysis procedure, from the quality control of data to the final validation going through normalization, feature transformation, feature selection, and model building. Each section proposes a set of guidelines and motivates the specific choice made for this particular study. Finally, the main guidelines that emerged from this study are the use of simple and effective techniques rather than complex non-linear models, the use of interpretable methods and the use of scalable computational solutions able to deal with multiplatform and multisource data. © 2008 Springer-Verlag Berlin Heidelberg.