Integrative Cluster Analysis in Bioinformatics |
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| Author:
| Abu-Jamous, Basel
Fa, Rui
Nandi, Asoke K. |
| ISBN: | 978-1-118-90655-2 |
| Publication Date: | Apr 2015 |
| Publisher: | John Wiley & Sons, Incorporated
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| Book Format: | Digital download |
| List Price: | Contact Supplier contact
Contact Supplier contact
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Book Description:
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Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery.
This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also...
More Description
Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery.
This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review of clustering analysis in bioinformatics from the fundamentals through to state-of-the-art techniques and applications.
Key Features:
- Offers a contemporary review of clustering methods and applications in the field of bioinformatics, with particular emphasis on gene expression analysis
- Provides an excellent introduction to molecular biology with computer scientists and information engineering researchers in mind, laying out the basic biological knowledge behind the application of clustering analysis techniques in bioinformatics
- Explains the structure and properties of many types of high-throughput datasets commonly found in biological studies
- Discusses how clustering methods and their possible successors would be used to enhance the pace of biological discoveries in the future
- Includes a companion website hosting a selected collection of codes and links to publicly available datasets