The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
An introduction to variable and feature selection
The Journal of Machine Learning Research
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Genome-wide association (GWA) studies, which typically aim to identify single nucleotide polymorphisms (SNPs) associated with a disease, yield large amounts of high-dimensional data. GWA studies have been successful in identifying single SNPs associated with complex diseases. However, so far, most of the identified associations do only have a limited impact on risk prediction. Recent studies applying SVMs have been successful in improving the risk prediction for Type I and II diabetes, however, a drawback is the poor interpretability of the classifier. Training the SVM only on a subset of SNPs would imply a preselection, typically by the p-values. Especially for complex diseases, this might not be the optimal selection strategy. In this work, we propose an extension of Adaboost for GWA data, the so-called SNPboost. In order to improve classification, SNPboost successively selects a subset of SNPs. On real GWA data (German MI family study II), SNPboost outperformed linear SVM and further improved the performance of a non-linear SVM when used as a preselector. Finally, we motivate that the selected SNPs can be put into a biological context.