Advanced Analysis Of Gene Expression Microarray Data (science, Engineering, And Biology Informatics)
by Aidong Zhang /
2006 / English / PDF
19.3 MB Download
This book focuses on the development and application of the latest
advanced data mining, machine learning, and visualization
techniques for the identification of interesting, significant, and
novel patterns in 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; and
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.
This book focuses on the development and application of the latest
advanced data mining, machine learning, and visualization
techniques for the identification of interesting, significant, and
novel patterns in 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; and
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.
