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Bioinformatics: Problem Solving Paradigms

By: Material type: TextTextPublication details: Berlin Springer 2008Description: xviii, 289 pages : illustrations ; 25 cmISBN:
  • 9783540785057
Subject(s): DDC classification:
  • 572.80285 SPE-B
Contents:
Core bioinformatics problems -- Turning to algorithmic problems -- Dynamic programming -- Intelligent data structures -- NP-hardness of core bioinformatics problems -- Approximation algorithms -- A selection of metaheuristics and various projects.
Summary: "There are fundamental principles for problem analysis and algorithm design that are continuously used in bioinformatics. This book concentrates on a clear presentation of these principles, presenting them in a self-contained, mathematically clear and precise manner, and illustrating them with lots of case studies from main fields of bioinformatics. Emphasis is laid on algorithmic "pearls" of bioinformatics, showing that things may get rather simple when taking a proper view into them. The book closes with a thorough bibliography, ranging from classic research results to very recent findings, providing many pointers for future research. Overall, this volume is ideally suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on its mathematical and computer science background."--Jacket.
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Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 572.80285 SPE-B (Browse shelf(Opens below)) Available DCB1588

Core bioinformatics problems -- Turning to algorithmic problems -- Dynamic programming -- Intelligent data structures -- NP-hardness of core bioinformatics problems -- Approximation algorithms -- A selection of metaheuristics and various projects.

"There are fundamental principles for problem analysis and algorithm design that are continuously used in bioinformatics. This book concentrates on a clear presentation of these principles, presenting them in a self-contained, mathematically clear and precise manner, and illustrating them with lots of case studies from main fields of bioinformatics. Emphasis is laid on algorithmic "pearls" of bioinformatics, showing that things may get rather simple when taking a proper view into them. The book closes with a thorough bibliography, ranging from classic research results to very recent findings, providing many pointers for future research. Overall, this volume is ideally suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on its mathematical and computer science background."--Jacket.

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