Target Selection: A New Learning Paradigm and Its Application to Genetic Association Studies

In this work, a new learning paradigm called target selection is proposed, which can be used to test for associations between a single genetic variable and a multidimensional, quantitative phenotype. In target selection, the task of a learning machine is to chose one out of several nominal target variables, as well as a probabilistic classification function for the selected target. For this new paradigm, a cost function is derived from the concept of mutual information and a learning algorithm is suggested. The significance of the generalization performance of the model learned using target selection is tested using a label permutation test. Here, the proposed target selection paradigm is applied to a genomic imaging study.

• Type: Conference
• Year: 2008
• Where: ICMLA
• Authors: Johannes Mohr, Sambu Seo, Imke Puis, Andreas Heinz, Klaus Obermayer
• Keywords: ICMLA 2008, Machine Learning, Nominal Target Variables, Paradigm Called Target, Target Selection