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.

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.

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.