Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
On the computation of the distribution of the square of the sample multiple correlation coefficient
Computational Statistics & Data Analysis
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Support vector regression with ANOVA decomposition kernels
Advances in kernel methods
Machine Learning
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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In recent years, a lot of attention is placed on the selection and evaluation of biomarkers in microarray experiments. Two sets of biomarkers are of importance, namely therapeutic and prognostic. The therapeutic biomarkers would give us information on the response of the genes to treatment in relation to the response of the clinical outcome to the same treatments, whereas the prognostic biomarkers enable us to predict the clinical outcome irrespective of treatments and other confounding factors. In this paper, we use different methods that allow for both linear and non-linear associations to select prognostic markers for depression, the response.