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Gene expression incorporating prior knowledge of gene functional groups into regularized discriminant analysis of microarray data.
| Content Provider | CiteSeerX |
|---|---|
| Author | Tai, Feng Pan, Wei |
| Abstract | Motivation: Discriminant analysis for high-dimensional and low-sample-sized data has become a hot research topic in bioinfor-matics, mainly motivated by its importance and challenge in applications to tumor classifications for high-dimensional microarray data. Two of the popular methods are the nearest shrunken centroids, also called predictive analysis of microarray (PAM), and shrunken centroids regularized discriminant analysis (SCRDA). Both methods are modifications to the classic linear discriminant analysis (LDA) in two aspects tailored to high-dimensional and low-sample-sized data: one is the regularization of the covariance matrix, and the other is variable selection through shrinkage. In spite of their usefulness, there are potential limitations with each method. The main concern is that both PAM and SCRDA are possibly too extreme: the covariancematrix in the former is restricted to be diagonal while in |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Discriminant Analysis Microarray Data Gene Expression Incorporating Gene Functional Group Low-sample-sized Data Shrunken Centroid Potential Limitation Predictive Analysis Classic Linear Discriminant Analysis Hot Research Topic Variable Selection Main Concern Popular Method Covariance Matrix High-dimensional Microarray Data |
| Content Type | Text |
