Advanced Analysis of Gene Expression Microarray Data

Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data. Specifically, the coverage includes the following state-of-the-art methods:

• Gene-based analysis: the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets

• Sample-based analysis: supervised and unsupervised methods for the reduction of the gene dimensionality to select significant genes. A series of approaches to disease classification and discovery are also described

• Pattern-based analysis: methods for ascertaining the relationship between (subsets of) genes and (subsets of) samples. Various novel pattern-based clustering algorithms to find the coherent patterns embedded in the sub-attribute spaces are discussed

• Visualization tools: various methods for gene expression data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more easily understood two- or three-dimensional space.