Methods for class prediction with high-dimensional gene expression data

An increasing amount of genomic data has become available. The work deals with class prediction with highdimensional gene expression data. Combining gene expression data with other data can improve the prediction of disease prognosis. The main part of the work is aimed at combining gene expression data with clinical data. We use logistic regression models that can be built through various regularized techniques. Generalized linear models enable us to combine models with different structure of data. It is shown that such a combination may yield more accurate predictions than those obtained based on the use of gene expression or clinical data alone. Suggested approaches are not computationally intensive.