Mourad Elloumi,Albert Y. Zomaya - Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications
-25%

Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications

Mourad Elloumi,Albert Y. Zomaya

ISBN: 9780470505199
Vydavateľstvo: Wiley
Rok vydania: 2011
Väzba: Hardback
Počet strán: 1080
Dostupnosť: Na sklade

Pôvodná cena: 180,18 €
Výstavná cena: 135,14 €(t.j. po zľave 25%)
(Cena je uvedená vrátane 10% DPH)
Katalógová cena: 117 GBP

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The most comprehensive, practical overview of algorithms in computational molecular biology Computational molecular biology has emerged from the Human Genome Project as an important discipline for academic research and industrial application. Due to the exponential growth?in the size of biological databases and the increasing intricacy of biological problems, high–performance algorithms are now necessary to deal with errors in biological sequences.?Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology is the first in–depth examination of these important algorithms, which are based on the newest and most improved approaches and techniques in the field. The book?s unique coverage offers both a wide range of information—from theintroductory fundamentals right up to the latest, most advanced levels of study—and enough technical depth to be of practical use to working professionals. Organized into seven parts, it addresses: Strings processing and application to biological sequences Analysis of biological sequences Motif finding and structure prediction Phylogeny reconstruction Microarray data analysis Analysis of genomes Analysis of biological networks Also featured are a combination of experiments and simulations that provide not only qualitative but also quantitative insights into this rich field. By covering a wide range of combinatorial problems that arise in computational molecular biology, this book enables researchers, computer scientists, life scientists, mathematicians, graduate students, and senior undergraduates to deal with more complex issues and richer data sets.