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Continuous Media Databases brings together in one place important
contributions and up-to-date research results in this fast moving
area. Continuous Media Databases serves as an excellent reference,
providing insight into some of the most challenging research issues
in the field.
Continuous Media Databases brings together in one place important
contributions and up-to-date research results in this fast moving
area. Continuous Media Databases serves as an excellent reference,
providing insight into some of the most challenging research issues
in the field.
The analysis of protein-protein interactions is fundamental to the
understanding of cellular organization, processes, and functions.
Proteins seldom act as single isolated species; rather, proteins
involved in the same cellular processes often interact with each
other. Functions of uncharacterized proteins can be predicted
through comparison with the interactions of similar known proteins.
Recent large-scale investigations of protein-protein interactions
using such techniques as two-hybrid systems, mass spectrometry, and
protein microarrays have enriched the available protein interaction
data and facilitated the construction of integrated protein-protein
interaction networks. The resulting large volume of protein-protein
interaction data has posed a challenge to experimental
investigation. This book provides a comprehensive understanding of
the computational methods available for the analysis of
protein-protein interaction networks. It offers an in-depth survey
of a range of approaches, including statistical, topological,
data-mining, and ontology-based methods. The author discusses the
fundamental principles underlying each of these approaches and
their respective benefits and drawbacks, and she offers suggestions
for future research.
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
- 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.
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