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![]( "Buchcover ISBN 9780471756491")
„... clearly written and understandable... researchers andstudents in the related areas will find the style and formatfamiliar and the content valuable.“ (E-STREAMS, September2007)
„... a building block on computational biology concepts tohelp researchers and students work on more innovative ideas.“(IEEE Distributed Systems Online, March 2007)
„... a good overview of the current state of computing inthese areas.“ (CHOICE, November 2006)
„... this book presents researchers in computational biology, bioinformatics, mathematics, statistics, and computer science withthe opportunity to explore this interdisciplinary researcharea...“ (Computing Reviews. com, September 27, 2006)
Parallel Computing for Bioinformatics and Computational Biology
Models, Enabling Technologies, and Case Studies
herausgegeben von Albert Y. ZomayaDiscover how to streamline complex bioinformatics applications withparallel computing
This publication enables readers to handle more complexbioinformatics applications and larger and richer data sets. As theeditor clearly shows, using powerful parallel computing tools canlead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution.
A broad range of bioinformatics applications is covered withdemonstrations on how each one can be parallelized to improveperformance and gain faster rates of computation. Current parallelcomputing techniques and technologies are examined, includingdistributed computing and grid computing. Readers are provided witha mixture of algorithms, experiments, and simulations that providenot only qualitative but also quantitative insights into thedynamic field of bioinformatics.
Parallel Computing for Bioinformatics and Computational Biology isa contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlinesdifficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in thefield and carefully edited to ensure a consistent approach and highstandard throughout the publication.
The work is organized into five parts:
* Algorithms and models
* Sequence analysis and microarrays
* Phylogenetics
* Protein folding
* Platforms and enabling technologies
Researchers, educators, and students in the field of bioinformaticswill discover how high-performance computing can enable them tohandle more complex data sets, gain deeper insights, and make newdiscoveries.
This publication enables readers to handle more complexbioinformatics applications and larger and richer data sets. As theeditor clearly shows, using powerful parallel computing tools canlead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution.
A broad range of bioinformatics applications is covered withdemonstrations on how each one can be parallelized to improveperformance and gain faster rates of computation. Current parallelcomputing techniques and technologies are examined, includingdistributed computing and grid computing. Readers are provided witha mixture of algorithms, experiments, and simulations that providenot only qualitative but also quantitative insights into thedynamic field of bioinformatics.
Parallel Computing for Bioinformatics and Computational Biology isa contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlinesdifficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in thefield and carefully edited to ensure a consistent approach and highstandard throughout the publication.
The work is organized into five parts:
* Algorithms and models
* Sequence analysis and microarrays
* Phylogenetics
* Protein folding
* Platforms and enabling technologies
Researchers, educators, and students in the field of bioinformaticswill discover how high-performance computing can enable them tohandle more complex data sets, gain deeper insights, and make newdiscoveries.
