Mining multiple phenotype structures underlying gene expression profiles

DNA microarray technology is now widely used in basic biomedical research for mRNA expression profiling and are increasingly being used to explore patterns of gene expression in clinical research. Automatically detecting phenotype structures from gene expression profiles can provide deep insight into the nature of many diseases as well as lead in the development of new drugs. While most of the previous studies focus on only mining empirical phenotype structure which the experiment controls, it is also interesting to detect possible hidden phenotype structures underlying gene expression profiles. Since the number of samples is usually limited, such data sets are very sparse in high-dimensional gene space. Furthermore, most of the genes of interest are buried in large amount of noise. Unsupervised phenotype structure discovery of such sparse high-dimensional data sets present interesting but challenging problems. In this paper, we propose the model of simultaneously mining both empirical and hidden phenotype structures from gene expression data. We demonstrate the effectiveness and efficiency of the proposed method on various real-world data sets.