|
|
Sequential Application of Feature Selection and Extraction for Predicting Breast Cancer Aggressiveness Host Publication: Computational Systems-Biology and Bioinformatics Authors: J. Taminau, S. Meganck, V. Cosmin Lazar, D. Y. Weiss-Solis, A. Coletta, N. Walker, H. Bersini and A. Nowé Publisher: Springer Publication Year: 2010 Number of Pages: 12 ISBN: 978-3-642-16749-2
Abstract: Breast cancer is a heterogenous disease with a large variance in prognosis of patients. It is hard to distinguish patients who would need adjuvant chemotherapy from those who can survive without. Using micro-array based technology and various feature selection techniques, a number of prognostic gene expression signatures have been proposed recently. It has been shown that these signatures outperform traditional clinical guidelines for estimating the prognosis. In this paper the applica- bility of state-of-the-art feature extraction methods together with feature selection methods is studied to develop more powerful prognosis estima- tors. In a first instance, feature selection is used to remove features not related with the clinical issue investigated. If the resulted dataset is still described by a high number of probes, feature extraction methods can be applied to further reduce the dimension of the data set. In addition we derived signatures using three independent data sets, containing in total 610 samples.
|
|
