One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. D...
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
Building classification models plays an important role in DNA mircroarray data analyses. An essential feature of DNA microarray data sets is that the number of input variables (gen...
Background: Human heart failure is a complex disease that manifests from multiple genetic and environmental factors. Although ischemic and non-ischemic heart disease present clini...
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis canno...