Abstract
One important application of microarray gene expression data is to study the relationship between the clinical phenotype of cancer patients and gene expression profiles on the whole-genome scale. The clinical phenotype includes several different types of cancers, survival times, relapse times, drug responses and so on. Under the situation that the subtypes of cancer have not been previously identified or known to exist, we develop a new kernel mixture modeling method that performs simultaneously identification of the subtype of cancer, prediction of theprobabilities of both cancer type and patient's survival, and detection of a set of marker genes onwhich to base a diagnosis. The proposed method is successfully performed on real data analysis and simulation studies.
