Abstract
The aim of my research is the identification of new approaches for dimension reduction and classification of high dimensional gene expression data. The problem of dimensionality reduction means reducing the large number of genes only on the important genes which are somehow bundled together.
The problem of classification means finding few genes from a large number of genes which are important for the classification of an uknown sample to a certain group (such as disease). I would like to establish both tasks, i.e. dimension reduction and classification, on methods of information theory.
The most known variables in information theory are Shannon entropy or mutual information. I would like to combine methods of information theory with the shrinkage method which is a generalization of the concept of the James-Stein estimate.